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Machine Learning Engineer vs Applied Scientist: 54% Skill Overlap

Gnana — Tue, 18 Aug 2026 00:51:52 +0000

The Skills That Define Each Role Get Priced in Opposite Directions

Machine Learning Engineer and Applied Scientist look like they should be the same job wearing two different name tags. Both anchor on Python and Machine Learning at nearly identical rates, and more than half of each role's top-30 skill set overlaps with the other's. We pulled every active posting for both roles on the InterviewStack.io job board as of August 2026, 5,665 for Machine Learning Engineer and 1,708 for Applied Scientist, and compared them skill by skill and dollar by dollar.

Look at what each role's own exclusive skills do to pay, though, and the two jobs split cleanly in half. Machine Learning Engineer's defining skills, MLOps, CI/CD, Kubernetes, Docker, mostly do not move the role's own salary at all. Applied Scientist's defining skills, C++, Java, Reinforcement Learning, NLP, add a consistent premium of over $11,000 each.

	Machine Learning Engineer	Applied Scientist
Median US base salary	$190,000	$185,500
Active postings	5,665	1,708
Top skill	Machine Learning (68.2%)	Machine Learning (64.1%)
Remote share	20.5%	16.0%
Entry-level share	4.4%	7.4%
Skill overlap (Jaccard)	54% shared (pairwise)	54% shared (pairwise)

Key Findings

Machine Learning Engineer and Applied Scientist share a Jaccard skill overlap of 0.54, well above the mid-range typical of this comparison series.

Median US base salary sits within $4,500 of each other: $190,000 for Machine Learning Engineer versus $185,500 for Applied Scientist, a 2.4% gap.

Machine Learning Engineer carries 3.32x the posting volume: 5,665 active listings versus 1,708 for Applied Scientist.

Eight of Machine Learning Engineer's nine exclusive skills price at or below its own $190,000 baseline; only Google Cloud clears it, by $1,300.

Six of Applied Scientist's seven exclusive skills add $11,100 to $11,400 over its own $185,500 baseline; the one outlier, Linux, prices $17,500 below it.

Entry-level share runs higher for the smaller role: 7.4% of Applied Scientist postings versus 4.4% for Machine Learning Engineer.

Amazon alone accounts for 14.5% of Applied Scientist's distinct openings in this snapshot, the largest single-employer concentration in this data.

What a Machine Learning Engineer and an Applied Scientist Actually Do

A Machine Learning Engineer spends most of the week making models survive contact with production traffic: packaging a trained model behind an API, wiring it into a CI/CD pipeline, monitoring it once live, and scaling the infrastructure on AWS, Azure, or Google Cloud. The exclusive skill list, MLOps, Docker, Kubernetes, CI/CD, reads like a deployment checklist. Browse live Machine Learning Engineer postings and that pattern holds across most listings.

An Applied Scientist works further upstream: designing the model, running the experiments (A/B Testing shows up in 17.2% of postings) that decide whether it's good enough to ship, and often writing performance-critical code in C++ or Java. Reinforcement Learning and NLP round out a skill set closer to a research curriculum than a deployment one. Browse live Applied Scientist postings to see the pattern.

One data-quality note: "Applied Scientist" is a broader title than "Machine Learning Engineer," and this dataset's sample includes some postings from adjacent scientific-research fields, bioinformatics, clinical research, geophysics, academic research fellowships, that use the same job title without doing applied ML work. The skill and salary figures below still skew cleanly toward ML/AI terms (Python, Machine Learning, Statistics, C++), so the aggregate numbers hold up, but expect some non-ML science roles mixed into the raw posting count if you browse the board directly.

Which Skills Do Both Roles Actually Share?

Python and Machine Learning anchor both roles at almost the same frequency, and that's the clearest true overlap in the data.

Machine Learning and Python sit at nearly identical rates on both sides; nearly everything else that clears the "shared skill" bar still leans toward one role or the other.

Skill	Machine Learning Engineer	Applied Scientist
Machine Learning	68.2%	64.1%
Python	60.7%	63.4%
PyTorch	36.9%	26.0%
Deep Learning	30.7%	33.7%
Algorithms	21.8%	33.5%
Statistics	13.2%	28.5%
AWS	29.7%	10.9%
Generative AI	27.0%	20.6%

Machine Learning and Python are the genuine bridge, close enough on both sides that switching roles wouldn't require relearning either one. Everything else tilts hard: Statistics runs more than twice as common for Applied Scientist (28.5% versus 13.2%), while AWS runs nearly three times as common for Machine Learning Engineer (29.7% versus 10.9%). Clearing the shared-skill threshold doesn't mean both roles lean on a skill the same way.

Generative AI and LLM-tagged skills sit well below Machine Learning itself for both roles (27.0% and 25.7% for Machine Learning Engineer, 20.6% and 19.6% for Applied Scientist). That gap measures who's explicitly hired to build AI systems, not who uses AI tools at all, postings only tag Generative AI or LLM skills when a role requires building or shipping one. Stack Overflow's 2025 Developer Survey puts general AI tool adoption at 84% of developers, with roughly half using AI tools daily, so the honest read is that both roles use AI tooling constantly; Machine Learning Engineer's modest tilt just reflects its more deployment-facing, RAG-and-API-heavy skill set.

Where Do Machine Learning Engineer and Applied Scientist Skills Diverge?

Machine Learning Engineer's exclusive skills describe how you ship a model. Applied Scientist's describe how you build one.

Exclusive to Machine Learning Engineer	Freq.	Exclusive to Applied Scientist	Freq.
MLOps	23.7%	C++	20.1%
Azure	20.4%	Java	16.2%
CI/CD	19.8%	Reinforcement Learning	15.0%
Google Cloud	17.6%	NLP	11.0%
Docker	15.9%	Linux	8.5%
Kubernetes	15.7%	Apache Spark	8.1%

The Machine Learning Engineer column (MLOps, CI/CD, Kubernetes, Docker) reads as a production-infrastructure checklist: package it, deploy it, scale it, keep it alive. The Applied Scientist column runs the opposite direction: C++ and Java signal performance-critical model code, Reinforcement Learning and NLP signal research specialization, and Apache Spark signals offline experimentation over an always-on service.

Which Role Pays More?

Barely either one, at least at the headline level, but the skills behind each median tell very different stories. These are US-only base salaries; equity, bonus, and sign-on pay are not disclosed in postings, so total compensation at top employers runs higher than either figure below.

Machine Learning Engineer's median US base salary sits $4,500 above Applied Scientist's, close enough that the two roles are effectively tied at the median.

Machine Learning Engineer's median US base salary is $190,000 across 1,481 postings with salary disclosed, versus $185,500 for Applied Scientist across 673 postings, a $4,500 (2.4%) gap, close enough to call a statistical tie.

The premium logic underneath is where the two roles split. On Machine Learning Engineer's own salary data, eight of its nine exclusive skills sit at or below the role's baseline: MLOps (-$5,200), Azure (-$14,500), CI/CD (-$6,000), Docker (-$16,000), RAG (-$2,500), APIs (-$9,000), Scalability (-$500), and Kubernetes (flat). Only Google Cloud clears it, by $1,300. The skills that define the job title don't buy a raise; they're table stakes for a much larger job market.

On Applied Scientist's own data, the pattern flips. C++, Java, Reinforcement Learning, and NLP (under both that tag and its full name, Natural Language Processing) each add $11,100 to $11,400 over the role's $185,500 baseline, and Apache Spark matches it. The one exception is Linux, which prices $17,500 below baseline. For Applied Scientist, the skills that define the job also get paid for.

Neither role's biggest premium comes from its own exclusive list, though. On Applied Scientist's data, JAX, a high-performance array-computing library, adds $46,400 to the role's baseline (n=48), the largest single-skill premium either role's data supports. On Machine Learning Engineer's data, JAX still pays, $24,000 over baseline (n=124), but RLHF pays more: $45,600 (n=43), nearly double JAX's premium for this role. Either way, the pattern holds: the biggest raises trace to shared AI specialties, not the toolkit that defines either job title.

Which Role Has More Job Openings?

Machine Learning Engineer posts 3.32x the volume: 5,665 active listings versus 1,708 for Applied Scientist. That size gap doesn't translate into an easier entry bar, though.

	Machine Learning Engineer	Applied Scientist
Entry-level	4.4%	7.4%
Mid-level	56.1%	53.5%
Senior	22.4%	21.9%
Staff	17.1%	17.2%
Remote	20.5%	16.0%
Hybrid	26.2%	25.5%
Onsite	58.4%	64.0%

Entry-level share is actually higher for the smaller, more research-heavy role (7.4% of Applied Scientist postings versus 4.4% for Machine Learning Engineer), so the larger job market isn't the easier one to break into. Geography adds a wrinkle: Applied Scientist runs more US-concentrated (56.8% versus 44.8%, with Machine Learning Engineer drawing a meaningfully larger share from India at 16.7%) and more onsite (64.0% versus 58.4%). Part of that traces to concentration: Amazon alone accounts for 14.5% of Applied Scientist's distinct openings here, the largest single-employer share for either role, so read its onsite and US-geography figures as leaning toward that employer's policies, not a flat market average.

Which Role Should You Choose?

Choose Machine Learning Engineer if you:

Want the larger, more open job market (3.32x the postings) and would rather build deployment expertise than a deep research background
Are drawn to the production side of ML: shipping, monitoring, and scaling models rather than designing them from scratch
Can accept that your core toolset (MLOps, CI/CD, Kubernetes) is table stakes rather than a pay lever; your ceiling comes from layering scarcer specialties like JAX or distributed training on top

Choose Applied Scientist if you:

Already have, or want to build, a deeper algorithmic and statistical foundation (Algorithms at 33.5% of postings, Statistics at 28.5%) alongside systems languages like C++ and Java
Want your specialization to translate directly into pay: C++, Java, Reinforcement Learning, and NLP each add roughly $11,100 to $11,400 over the role's baseline
Are comfortable with a smaller, more concentrated, more onsite-heavy job pool in exchange for research-facing work and a nearly identical median salary

How to Use This in Your Job Search

If Applied Scientist's deeper bar (Algorithms, Statistics, C++) stands between you and that premium, InterviewStack's interactive courses cover the ML theory, statistics, and systems-programming foundations most postings assume. Already comfortable there? Practice with AI mock interviews built around real ML system-design and applied-research loops, and drill the Question Bank on MLOps pipelines, reinforcement learning, and NLP. When you're ready to apply, browse live Machine Learning Engineer openings or Applied Scientist openings.

For a full breakdown of either role on its own, see Machine Learning Engineer Skills in 2026 and Applied Scientist's entry-level breakdown. If AI Engineer or Data Scientist are also on your shortlist, AI Engineer vs Applied Scientist and Data Scientist vs Machine Learning Engineer cover two more angles on the same fork.

FAQ

Q. What is the salary difference between Machine Learning Engineer and Applied Scientist in 2026?

Machine Learning Engineer's median US base salary is $190,000 (n=1,481 postings with US salary disclosed), just $4,500 (2.4%) above Applied Scientist's $185,500 median (n=673). Equity, bonus, and sign-on pay are not disclosed in postings and are not part of these figures.

Q. Do Machine Learning Engineer and Applied Scientist share the same skills?

More than half. The two roles have a Jaccard overlap of 0.54 on their top-30 skills, one of the higher overlap scores in InterviewStack's role-comparison data. Machine Learning (68.2% of Machine Learning Engineer postings, 64.1% of Applied Scientist postings) and Python (60.7% versus 63.4%) anchor both roles at nearly identical rates.

Q. Which skills are exclusive to Machine Learning Engineer postings?

MLOps (23.7% of postings), Azure (20.4%), CI/CD (19.8%), Google Cloud (17.6%), Docker (15.9%), Kubernetes (15.7%), RAG (13.9%), APIs (13.7%), and Scalability (13.0%) all post often enough in Machine Learning Engineer listings to count as real differentiators, and too rarely in Applied Scientist listings to register there.

Q. Which skills are exclusive to Applied Scientist postings?

C++ (20.1% of postings), Java (16.2%), Reinforcement Learning (15.0%), NLP (11.0%, or 8.6% under the full term Natural Language Processing), Linux (8.5%), and Apache Spark (8.1%) all post often enough in Applied Scientist listings to count as real differentiators, and too rarely in Machine Learning Engineer listings to register there.

Q. Do Machine Learning Engineer's or Applied Scientist's differentiator skills pay more?

Applied Scientist's do, by a wide margin. On Applied Scientist's own salary data, C++, Java, Reinforcement Learning, and NLP each add roughly $11,100 to $11,400 over the role's $185,500 baseline. On Machine Learning Engineer's own salary data, its defining skills (MLOps, Azure, CI/CD, Docker, APIs) mostly sit at or below the role's $190,000 baseline; only Google Cloud clears it, and only by $1,300.

Q. Which role has more open positions, Machine Learning Engineer or Applied Scientist?

Machine Learning Engineer, by a wide margin: 5,665 active postings versus 1,708 for Applied Scientist, a 3.32x volume gap. Entry-level share actually runs higher for Applied Scientist (7.4% versus 4.4%), so the smaller role isn't the harder one to break into at the bottom.

Q. Do both roles require generative AI or LLM skills?

Both show a gap between explicit posting language and real-world AI tool use. Generative AI appears in 27.0% of Machine Learning Engineer postings and 20.6% of Applied Scientist postings, with LLM-tagged skills running similarly higher for Machine Learning Engineer. But Stack Overflow's 2025 Developer Survey puts general AI-assisted tool adoption at 84% of developers overall, so most practitioners in both roles are almost certainly using AI tools daily regardless of what any single job posting states.

The Real Choice Isn't About Pay

Machine Learning Engineer and Applied Scientist land within $4,500 of each other at the median and share more than half their top skill set, so pay alone won't settle which one to pursue. The real fork is what your specialization buys you. Machine Learning Engineer's defining skills, MLOps, CI/CD, Kubernetes, are table stakes for a job market 3.32x the size, not a salary lever. Applied Scientist's defining skills, C++, Java, Reinforcement Learning, NLP, consistently add over $11,000 each in a smaller, more concentrated, more onsite pool. Pick the skill set whose logic matches how you want to be paid, not just what the paycheck says today.

Network Engineer vs Site Reliability Engineer: No Trade-off Here

Gnana — Sun, 09 Aug 2026 16:57:14 +0000

Every Advantage Except Volume Belongs to SRE

Most role comparisons force a trade-off: one side pays more, the other has more open jobs or a lower bar to entry. Network Engineer and Site Reliability Engineer (SRE) don't split that way. We looked at every active posting for both roles on the InterviewStack.io job board as of July 2026, 4,133 for Network Engineer and 3,487 for SRE, and SRE comes out ahead on pay, remote access, and seniority. Network Engineer's one clear advantage is more jobs to begin with.

That's not the same as the roles being interchangeable. The skill sets barely overlap (a 25% Jaccard similarity across each role's top-30 skills), and what each role asks for reflects two different jobs wearing adjacent-sounding titles: one keeps the network running, the other keeps everything that runs on top of it running. The gap in pay and flexibility tracks that difference, not a market inefficiency.

	Network Engineer	Site Reliability Engineer (SRE)
Median US base salary	$128,200	$150,000
Active postings analyzed	4,133	3,487
Top skill	Monitoring (44.2%)	Automation (55.5%)
Remote share	9.7%	18.6%
Entry-level share	2.7%	1.9%
Skill overlap (Jaccard)	25% shared (pairwise)	25% shared (pairwise)

Key Findings

SRE pays a $21,800 (14.5%) higher US base salary median than Network Engineer: $150,000 vs $128,200.

SRE offers nearly double the remote share of Network Engineer (18.6% vs 9.7%), and half of all SRE postings (50.0%) are hybrid or remote versus 37.0% for Network Engineer.

Network Engineer postings outnumber SRE postings 1.19x (4,133 vs 3,487 active listings analyzed).

The two roles share only a 25% Jaccard overlap across their top-30 skill lists.

SRE postings skew senior: 48.6% are senior or staff level, versus 31.7% for Network Engineer, which concentrates at mid-level (65.5%).

Ten skills are effectively exclusive to each side: Network Engineer's cluster is protocol and perimeter work (Firewalls 36.4%, BGP 29.9%, Network Security 29.6%, OSPF 25.9%); SRE's is platform tooling (Observability 43.2%, Kubernetes 39.9%, CI/CD 34.9%, Grafana 26.0%).

Network Engineer's core protocol specialties pay only a modest premium: BGP, OSPF, and Network Security each add about $6,000 over the role's own $128,200 baseline.

Keeping the Network Up vs Keeping the Whole System Up

A Network Engineer's day centers on the wires, routes, and perimeter that carry everything else: configuring BGP (the protocol that exchanges routing information between networks) and OSPF (a routing protocol for finding the best path within a single network) sessions, hardening firewalls, troubleshooting VPN tunnels, and keeping WAN links up when a data center loses connectivity. The work sits closer to civil engineering than software: build the roads, then defend them.

A Site Reliability Engineer inherits the traffic once it reaches the application. Their day runs through dashboards built on Prometheus (an open-source metrics and alerting toolkit) and Grafana, deploy pipelines, and the on-call rotation that owns incident response when a service degrades. The mandate is uptime for the whole system, not just the network layer underneath it.

What Skills Do Both Roles Actually Share?

Both roles now expect a baseline of software-adjacent skill, visible in live postings on the job board.

The 15 most common skills across both roles' top-30 lists, Network Engineer in one color, SRE in the other.

Skill	Network Engineer	SRE
Monitoring	44.2%	55.3%
Automation	33.1%	55.5%
Python	22.2%	49.2%
AWS	19.2%	39.7%
Linux	17.6%	34.3%
Terraform	7.9%	34.7%

None of these are neutral 50/50 splits, every one of the 12 skills that clears the shared-skill threshold is more common on the SRE side, though common enough on the Network Engineer side that a networking background isn't starting from zero for an SRE move. Terraform shows the widest swing, 7.9% versus 34.7%, a roughly 4.4x gap, the clearest sign infrastructure-as-code work is far more central to SRE than to networking today.

Which Skills Are Exclusive to Each Role?

Network Engineer's exclusive cluster is routing-and-perimeter protocol work: Firewalls (36.4%), BGP (29.9%), Network Security (29.6%), OSPF (25.9%), and VPN (21.6%) barely register in SRE postings. These keep packets moving and unauthorized traffic out, deep and narrow expertise most software roles never touch. See Network Engineer roles calling for BGP for where that expertise gets used.

SRE's exclusive cluster is almost entirely platform-observability tooling: Observability itself (43.2%), Kubernetes (39.9%), CI/CD (34.9%), Grafana (26.0%), and Infrastructure as Code (25.1%), the toolkit for running and watching a distributed system in production, not for building the network it runs on.

Which Role Pays More, and Why?

SRE earns a $21,800 (14.5%) premium over Network Engineer at the median: $150,000 vs $128,200 in US base salary, disclosed across 896 and 1,322 postings respectively. These are base salaries only; equity, bonus, and sign-on aren't disclosed in postings, so total comp at senior levels runs higher on both sides than these numbers suggest.

Overall median US base salary plus a handful of shared skills, priced separately for each role.

Part of the gap is seniority mix, not the job itself: 48.6% of SRE postings ask for senior or staff candidates versus 31.7% for Network Engineer. (One caveat on that figure: title-based classification for SRE pulls in some "Reliability Engineer" and "Operations Engineer" postings from industrial, energy, defense, and hardware-testing fields outside software, roles like offshore-reliability or RF-reliability engineers, and those fields have their own Senior/Staff/Principal titling conventions, which may modestly inflate the senior/staff share above. Genuine software SRE hiring skews senior in its own right, so the direction of the finding likely still holds.) But skill-level pay tells a sharper story. Network Engineer's own protocol specialties barely move the needle: BGP, OSPF, and Network Security each pay about $6,000 above baseline. Among the smaller set of postings tagged "Network Administration" (n=93 with salary disclosed), the median sits at $97,500, $30,700 below baseline. Deep protocol expertise buys a little; the generic label buys a discount.

SRE runs the opposite way. Postings that explicitly tag "Site Reliability Engineering" (n=206) pay $179,500, a $29,500 premium over SRE's own baseline, meaning naming the specialty explicitly is rewarded, not just doing SRE-adjacent DevOps work under a different title.

Explicit AI tags are rare in both datasets, below the frequency needed to crack either top-30 list, but where they show up they price like scarce expertise: Machine Learning carries a $163,000 median in Network Engineer postings (+$34,800, n=66) and $190,000 in SRE postings (+$40,000, n=45). That's the build-AI layer, narrow and well-paid. It says nothing about the ambient layer, AI-assisted config review, log triage, and troubleshooting now expected across technical work regardless of posting language. Stack Overflow's 2025 survey found 84% of developers already use or plan to use AI tools. Our prior SRE-specific analysis found 11.4% of postings explicitly require generative-AI skills (a separate dataset, cited for context); Network Engineer's AI story reads more organizational than individual so far, covered in how AI is changing Network Engineer work.

Which Role Has More Openings, and Who Can Get In?

Network Engineer carries the bigger candidate pool: 4,133 active postings versus 3,487 for SRE, a 1.19x edge. Neither is an easy first job: entry-level share is 2.7% for Network Engineer and 1.9% for SRE, close enough to be a rounding error. The roles genuinely differ above entry level. Network Engineer concentrates hard at mid-level (65.5%), senior plus staff at 31.7%. SRE is flatter and older: 49.5% mid-level, but 48.6% senior or staff, roughly one and a half times Network Engineer's share. Read alongside the pay gap, this reads less like "SRE pays more for the same work" and more like "SRE postings ask for a more experienced candidate."

The flexibility gap follows the same pattern. Just over a third of Network Engineer postings (37.0% hybrid plus remote) offer anything besides a desk on-site; SRE clears the halfway mark (50.0%), with remote alone nearly double Network Engineer's share (18.6% vs 9.7%). Both roles concentrate in the US (53.8% Network Engineer, 41.0% SRE), with India the clear second market for each (6.3% and 11.4%).

Pick the Layer You Want to Own

Choose Network Engineer if you:

Want deep protocol and perimeter expertise (BGP, OSPF, firewalls, VPN) to be your primary craft, not a secondary skill.
Are comfortable with a role that's still mostly on-site (65.9%) and want the larger, steadier pool of open positions today (4,133 postings, a 1.19x edge over SRE).
Are earlier in a networking-focused career and want to build protocol depth before branching into cloud or automation work.

Choose Site Reliability Engineer if you:

Want ownership of the whole production system, deploys, uptime, incident response, not just the network layer underneath it.
Value remote and hybrid flexibility (50.0% of postings) and are prepared to compete for a role that skews senior (48.6% senior or staff).
Already have, or want to build, a software-and-cloud toolkit (Kubernetes, Python, CI/CD, observability) rather than protocol-specific depth.

Put This Data to Work in Your Prep

Whichever side you're leaning toward, closing the gap between "I read the skill list" and "I can talk through it in an interview" takes deliberate practice. Practice with AI mock interviews built around scenarios each role actually faces, incident response and escalation for SRE, protocol troubleshooting and change management for Network Engineer. The Question Bank breaks both domains down by concept for targeted drilling, and our interactive courses build the underlying networking and systems foundations either path assumes you already have.

For a deeper look at either role on its own, see Network Engineer and SRE skills demand, or compare Network Engineer against Cloud Engineer and SRE against Systems Engineer for adjacent paths worth weighing.

FAQ

Q. Does Network Engineer or Site Reliability Engineer pay more in 2026?

Site Reliability Engineer pays more. Among US postings with salary disclosed, the median SRE base salary is $150,000 (n=896) versus $128,200 for Network Engineer (n=1,322), a $21,800 (14.5%) premium. Both figures are base salary only; equity, bonus, and sign-on are not disclosed in postings.

Q. How much do Network Engineer and Site Reliability Engineer skill sets overlap?

Not much. The two roles share a 25% Jaccard overlap across their top-30 skill lists. Twelve skills, including Monitoring, Automation, and Python, clear the shared-skill threshold in both roles, but ten skills are exclusive to each side: protocol and perimeter skills (BGP, OSPF, Firewalls, VPN) for Network Engineer, and platform tooling (Kubernetes, Observability, CI/CD, Grafana) for SRE.

Q. Is Network Engineer or Site Reliability Engineer easier to break into?

Neither is an easy first job. Entry-level postings make up just 2.7% of Network Engineer listings and 1.9% of SRE listings. Network Engineer concentrates heavily at mid-level (65.5% of postings), while SRE postings skew more senior overall, with 48.6% at senior or staff level versus 31.7% for Network Engineer.

Q. Which role offers more remote work, Network Engineer or SRE?

SRE. Remote postings make up 18.6% of SRE listings versus 9.7% for Network Engineer, and combined hybrid-plus-remote share clears half of all SRE postings (50.0%) compared to 37.0% for Network Engineer.

Q. Are there more Network Engineer or Site Reliability Engineer jobs?

Network Engineer has more active postings: 4,133 versus 3,487 for SRE, a 1.19x edge, based on active listings analyzed on the InterviewStack.io job board in July 2026.

Q. Do Network Engineer or Site Reliability Engineer postings require AI or machine learning skills?

Rarely, and only as an explicit requirement. Machine Learning doesn't crack the top-30 skill list for either role, though where it is tagged, it pays a premium in both (roughly +$34,800 for Network Engineer, +$40,000 for SRE, over each role's own baseline). That explicit tag is a floor, not the whole picture: ambient AI-assisted tooling for config review, log triage, and troubleshooting is now a baseline expectation across technical roles, tracked by developer surveys rather than job-posting language.

Q. What's the single biggest difference between Network Engineer and Site Reliability Engineer work?

Scope. Network Engineer owns the network layer itself: routing, firewalls, VPNs, and physical or virtual connectivity. SRE owns the reliability of the whole production system running on top of that network: deploys, incident response, and uptime. The skill sets reflect that split more than they reflect a shared 'infrastructure' label.

A Thin but Real Case for Network Engineering

Site Reliability Engineer wins the pay comparison, the flexibility comparison, and the seniority ceiling. Network Engineer's real edge is narrower: more open roles today (4,133 vs 3,487), and a specialty, BGP, OSPF, firewalls, that stays relevant as long as networks need dedicated owners. Cloud-native tooling hasn't replaced that work, it's just priced lower than platform engineering right now. If income and flexibility are the priority, the data points toward SRE, with the caveat that nearly half its postings expect senior-level experience. If access and volume matter more, Network Engineer remains the larger, steadier door in. Browse live Network Engineer openings or SRE openings on InterviewStack.io.

Frontend Developer vs Mobile Developer: The Overlap Skews Mobile

Gnana — Wed, 29 Jul 2026 00:57:08 +0000

The Smaller Role Sets the Shared Skill Bar

Frontend Developer and Mobile Developer read like two branches of the same tree: both build the software a user directly touches, both ship on a sprint cadence, both get lumped into the same "web or app" career fork. Comparing every active posting for each role on the InterviewStack.io job board as of July 2026 (5,276 Frontend Developer postings, 2,853 Mobile Developer postings), the skills the two roles genuinely share do not skew toward Frontend, the bigger, more general-sounding role. They skew toward Mobile.

Of the 15 skills that clear a 5% frequency floor in both roles, from Code Review and CI/CD to Git and APIs, 10 show up more often in Mobile Developer postings than in Frontend Developer postings. Frontend keeps the lead only on its own core language stack (JavaScript, React, TypeScript) plus Agile and, marginally, Scalability. Everything else in that shared column belongs more to the smaller role than the bigger one: code review, CI/CD, Git, debugging, even plain API work.

	Frontend Developer	Mobile Developer
Median US base salary	$140,000	$154,800
Active postings	5,276	2,853
Top skill	JavaScript (50.2%)	Android (64.2%)
Remote share	20.0%	32.5%
Entry-level share	10.7%	2.2%
Skill overlap (Jaccard)	33% shared (pairwise)	33% shared (pairwise)

Key Findings

Of the 15 skills shared by both roles at meaningful frequency, 10 (including Code Review, CI/CD, Git, and APIs) appear more often in Mobile Developer postings than Frontend Developer postings.

Mobile Developer's median US base salary is $154,800, $14,800 more than Frontend Developer's $140,000, a gap equal to 9.56% of Mobile's own baseline (n=330 vs. n=595 US postings).

Frontend Developer postings outnumber Mobile Developer postings 1.85 to 1 (5,276 vs. 2,853 active postings).

Only 2.2% of Mobile Developer postings are entry-level, versus 10.7% for Frontend Developer, roughly a 5x gap in first-rung access.

Mobile Developer postings are more remote-friendly: 32.5% remote versus 20.0% for Frontend Developer.

Design Systems is Frontend Developer's highest-paying skill ($183,000, +$43,000 over baseline, n=83); Observability is Mobile Developer's ($200,000, +$45,200, n=39).

Frontend's own top skill, JavaScript (50.2% of postings), pays exactly the role's $140,000 baseline; three of Mobile's four platform languages (iOS, Kotlin, Swift) pay at or below its $154,800 baseline.

Two Different Products, One Overlapping Toolkit

Frontend Developers build the interface layer of a web product: the component library, the page that renders in a browser, the state that keeps a UI consistent as data changes. The job pulls in real backend range too: AWS, Node.js, SQL, and Python all clear meaningful frequency as exclusive-to-Frontend skills, more than markup and styling alone.

Mobile Developers build for one of two closed ecosystems, Google Play or the App Store, or bridge both with React Native. Browse current Mobile Developer openings and the skill list is unambiguous: Android, iOS, Kotlin, Swift, MVVM (a UI pattern separating screen logic from business logic), Jetpack Compose (Android's declarative UI toolkit), and SwiftUI (Apple's declarative UI framework) all sit exclusive to this role. App-store review, crash reporting, and platform API changes impose a release discipline that likely explains why code review and CI/CD skew toward Mobile despite Frontend being the larger role.

Neither role's top skills carry an explicit AI or ML tag, so postings are silent on that axis for both. That silence is not absence: Stack Overflow's 2025 Developer Survey puts Frontend Developers at the top of every role surveyed for AI-tool adoption (69%), Mobile Developers close behind (65%), both above the all-role daily-use baseline (51%). The 4-point gap is real but small: both roles have already normalized coding-assistant use as a baseline expectation, regardless of what any single posting states.

Which Skills Do Both Roles Actually Share?

Frontend Developer postings demand JavaScript in a majority of cases (50.2%) and lean heavily on React and TypeScript too; Mobile Developer postings mention all three, but as a meaningful minority rather than a leading share (JavaScript sits at 18.6% of Mobile postings, a React Native footprint rather than the role's center of gravity). Both roles expect Code Review, CI/CD, Git, User Experience, and API work as standard practice. The direct answer to what transfers if you switch roles: the general software-delivery layer transfers cleanly; the language-specific layer (JavaScript, React, TypeScript, Angular) does not.

Frontend leads on the core web language stack; Mobile leads on most of the "shared" software-delivery practices.

Skill	Frontend Developer	Mobile Developer	Leans toward
JavaScript	50.2%	18.6%	Frontend
React	44.9%	15.8%	Frontend
TypeScript	37.4%	18.6%	Frontend
Code Review	21.8%	30.2%	Mobile
CI/CD	23.3%	27.6%	Mobile
Git	23.2%	27.1%	Mobile
User Experience	22.5%	26.2%	Mobile
APIs	22.1%	26.1%	Mobile
Java	11.2%	22.4%	Mobile
Restful APIs	9.8%	23.1%	Mobile

Java and Restful APIs are the sharpest splits inside the "shared" set: both appear roughly twice as often in Mobile postings, likely reflecting legacy Android codebases and backend-integration work that a Frontend role rarely touches directly. If you already work in Frontend and are considering Mobile, the code-review and CI/CD habits carry over; the language does not.

Where Do the Two Skill Sets Split?

Below the shared floor, the two roles look almost unrelated. Frontend's exclusive cluster is markup, styling, and web-platform concerns; Mobile's is two entire native ecosystems plus the bridge between them.

Exclusive to Frontend Developer: CSS (35.5%), HTML (28.2%), Angular (22.2%), Accessibility (16.3%), HTML5 (15.4%), AWS (15.2%), Node.js (15.1%), SQL (12.2%).

Exclusive to Mobile Developer: Android (64.2%), iOS (59.2%), Kotlin (36.7%), Swift (31.3%), React Native (27.1%), MVVM (18.1%), Android Development (17.3%), SwiftUI (13.0%).

The overlap coefficient across both roles' top-30 skill sets lands at 0.33, a mid-range figure, not an extreme in either direction. What it hides is composition: Mobile's exclusive list is one coherent platform stack; Frontend's spills into infrastructure and data skills that suggest broader role definitions at the companies posting these jobs.

Frontend Developer vs Mobile Developer: Which Role Pays More?

Mobile Developer pays more. Among US postings, where wage-transparency laws produce the most consistent salary disclosure, Mobile Developer's median base salary is $154,800 versus $140,000 for Frontend Developer, a $14,800 gap equal to 9.56% of Mobile's own baseline. These figures are base salary only; equity, bonus, and sign-on are not disclosed in job postings and are not reflected here, so total compensation at senior levels runs higher than either number suggests.

Mobile's US base salary baseline sits above Frontend's, but the highest-paid skills in both roles cluster well above either median.

Neither role's own headline skill is its best-paid one. Frontend's top skill, JavaScript (50.2% of postings), pays exactly the role's $140,000 baseline (n=278); CSS does too. Mobile's platform languages mostly sit at or below its $154,800 baseline (iOS -$3,500, Kotlin -$7,900, Swift -$2,500); only Android clears it, and just barely, at +$5,200.

Frontend Developer's highest-paying skills (US base salary, baseline $140,000):

Skill	Median USD	Premium	Sample size
Design Systems	$183,000	+$43,000	83
Prototyping	$172,500	+$32,500	38
Frontend Architecture	$172,500	+$32,500	38
PostgreSQL	$170,000	+$30,000	37
React	$169,100	+$29,100	243
TypeScript	$169,100	+$29,100	236
Data Structures	$169,100	+$29,100	28
Tailwind	$166,300	+$26,300	26
User Experience	$165,000	+$25,000	168
Observability	$165,000	+$25,000	39

This list excludes two entries that pay more per the raw data but aren't credible as frontend technical skills: "Frontend Development" ($180,000, n=31), which just restates the role title rather than naming a distinct skill, and "Compensation and Benefits" ($175,000, n=35), a non-technical HR tag that plausibly reflects the retail "front end" contamination noted below rather than genuine frontend engineering work. Every remaining entry down to the $165,000 tier clears a sample floor of at least 25 postings and is a real technical or design skill, including PostgreSQL and Data Structures, which shows this role's pay ceiling isn't purely a UI-layer story; some of the best-paid Frontend Developer postings expect data-layer and CS-fundamentals range on top of the component work. The ten entries above are the highest-paying skills that hold up as real frontend technical work.

Mobile Developer's highest-paying skills (US base salary, baseline $154,800):

Skill	Median USD	Premium	Sample size
Observability	$200,000	+$45,200	39
Concurrency	$194,100	+$39,300	28
User Experience	$183,800	+$29,000	78
Jetpack Compose	$180,500	+$25,700	57
SwiftUI	$180,000	+$25,200	55
Monitoring	$177,500	+$22,700	27
TypeScript	$175,100	+$20,300	38
Automated Testing	$174,500	+$19,700	26
A/B Testing	$173,300	+$18,500	28
Code Review	$171,000	+$16,200	73
React Native	$170,000	+$15,200	33
CI/CD	$169,300	+$14,500	66
APIs	$169,100	+$14,300	89
JavaScript	$160,300	+$5,500	28
Android	$160,000	+$5,200	219
Automation	$160,000	+$5,200	41

Observability and User Experience clear a real premium in both roles independently, evidence that production-monitoring literacy and design fluency pay off regardless of platform. Every Mobile Developer skill that prices above the $154,800 baseline is in the table above, sixteen entries in total, each backed by at least 26 postings; nothing above baseline was left out. A separate cluster of eight lower-frequency skills, including MVVM, Java, and Git, all report an identical $62,400 median, well under half the baseline. That flat, repeated value across otherwise-unrelated skills reads as a data artifact (a rounding or imputation collision) rather than a genuine signal that these particular skills pay less than the rest of the stack.

Which Role Has More Open Jobs?

Frontend Developer, by a wide margin: 5,276 active postings versus 2,853 for Mobile Developer, a 1.85x volume advantage. That larger pool comes with an easier door in, not just more of the same job: 10.7% of Frontend postings are tagged entry-level versus just 2.2% for Mobile, roughly a 5x gap. If you are trying to land a first engineering job, Frontend is the more forgiving market on paper.

Mobile compensates with flexibility. 32.5% of Mobile Developer postings are remote versus 20.0% for Frontend, and Frontend is markedly more onsite-heavy overall (57.8% versus 46.3%). Geographically, both roles concentrate in the US (29.6% Frontend, 21.1% Mobile) and India (13.2% Frontend, 10.1% Mobile), with Frontend slightly more US-weighted.

A data caveat on that volume and entry-level gap: "Front End" is a title collision, not just a skill collision. Albertsons Companies, a grocery and retail chain, is the single largest employer in this Frontend Developer dataset (356 postings, 6.8% of the role), and discount retailer Ollie's also appears in the top 15 (60 postings, 1.1%). In retail, "front end" commonly names the checkout and customer-service area of a store, not a software team, and this dataset shows the fingerprints: "customer service" is a top-30 Frontend Developer skill (10.1% of postings) and both "merchandising" and "compensation and benefits" appear in the salary-by-skill breakdown, none of which describe frontend engineering work. Some share of Frontend Developer's postings count, and plausibly its entry-level share specifically, reflects retail front-end staff swept in by the shared title rather than frontend software engineers. A smaller version of the same collision touches Mobile Developer: "mobile" also collides with facilities/property-management roles ("mobile engineer" as a roving maintenance title) in unrelated industries, and Jones Lang LaSalle, a commercial real-estate firm, appears among Mobile Developer's top employers (23 postings, 0.8% share). Treat the 1.85x volume gap and the 10.7%-vs-2.2% entry-level comparison as directionally right but not exact.

What This Decision Actually Hinges On

Choose Frontend Developer if you:

Want the more accessible entry point. The job pool is 1.85x larger and the entry-level share (10.7%) is roughly 5x Mobile's.
Already know JavaScript, React, or TypeScript and want to build on that foundation rather than start a new platform stack.
Want room to grow sideways into backend-adjacent work; AWS, Node.js, SQL, and Python all show up at meaningful frequency in these postings.

Choose Mobile Developer if you:

Want the higher salary ceiling and are willing to compete for a narrower entry door (2.2% entry-level share).
Want to specialize in one native platform (Kotlin/Android, Swift/iOS) or the React Native bridge, rather than the broader web stack.
Value remote flexibility. Mobile postings are remote 32.5% of the time versus 20.0% for Frontend.

How to Use This in Your Job Search

Whichever direction you're leaning, practice with AI mock interviews built around the actual skill stack for the role you're targeting, whether that's a component-architecture discussion for Frontend or a concurrency and memory-management deep dive for Mobile.

Both roles reward depth over breadth once you clear the table-stakes layer. Use the Question Bank to drill the topics that showed up as real salary differentiators here: design systems and frontend architecture for Frontend Developer, concurrency and platform-specific UI frameworks for Mobile Developer.

If you're starting from near zero on either stack, our interactive courses cover the foundations, React and modern JavaScript for Frontend, Kotlin and Swift fundamentals for Mobile, before you start interviewing.

Browse live Frontend Developer openings or Mobile Developer openings on InterviewStack.io, or filter further, such as Frontend roles asking for React. For a deeper per-role breakdown, see our Frontend Developer skills report and Mobile Developer skills report.

FAQ

Q. Does Frontend Developer or Mobile Developer pay more in 2026?

Mobile Developer's median US base salary is $154,800, about $14,800 more than Frontend Developer's $140,000, a gap equal to 9.56% of Mobile's own baseline, based on 330 and 595 postings respectively with US salary disclosed. Frontend Developer still posts 1.85 times more jobs overall.

Q. What skills do Frontend Developer and Mobile Developer postings share?

The two roles clear a 5% shared-skill threshold on 15 skills, including JavaScript, React, TypeScript, Code Review, CI/CD, Git, User Experience, and APIs. Ten of those 15 actually appear more often in Mobile Developer postings than in Frontend Developer postings, despite Mobile being the smaller role.

Q. Is Mobile Developer harder to break into than Frontend Developer?

Yes. Only 2.2% of Mobile Developer postings are tagged entry-level, versus 10.7% for Frontend Developer, roughly a 5x gap. Frontend Developer is the more accessible first job of the two.

Q. What skills are exclusive to Mobile Developer?

Android (64.2% of postings), iOS (59.2%), Kotlin (36.7%), Swift (31.3%), and React Native (27.1%) are Mobile Developer's most common exclusive skills, none of which clear meaningful frequency in Frontend Developer postings.

Q. What skills are exclusive to Frontend Developer?

CSS (35.5%), HTML (28.2%), Angular (22.2%), Accessibility (16.3%), and HTML5 (15.4%) are Frontend Developer's most common exclusive skills. Backend-adjacent skills like AWS, Node.js, SQL, and Python also show up at meaningful frequency, signaling that some Frontend Developer postings expect full-stack range.

Q. Which role offers more remote work?

Mobile Developer. 32.5% of Mobile Developer postings are remote versus 20.0% for Frontend Developer, and Frontend Developer is more onsite-heavy overall (57.8% versus 46.3%).

Q. Which role has more open jobs right now?

Frontend Developer, by a wide margin. It has 5,276 active postings versus 2,853 for Mobile Developer, a 1.85x volume advantage, even though Mobile pays more at the median.

The Bigger Role Doesn't Set the Bar

Frontend Developer is the larger, more accessible market; Mobile Developer is the smaller, better-paid, more remote-friendly one. But the skills the two roles supposedly share don't belong to the bigger role by default, they belong to whichever role's daily work demands more of them. Here, that's Mobile. Before assuming a "shared skill" transfers evenly in either direction, check which role actually leans on it more.

AI Engineer vs Business Intelligence Analyst: $36,900 Apart

Gnana — Sun, 26 Jul 2026 03:47:33 +0000

A $36,900 Gap Built on Two Different Vocabularies

AI Engineer postings pay a median $165,000 US base salary; Business Intelligence Analyst postings pay $128,100, both figures base-only, with no equity, bonus, or sign-on disclosed in postings. That's a $36,900 gap, and it's the number most people ask about first. The more useful story is why the gap exists: these two roles barely speak the same vocabulary. We looked at every active posting for both roles on the InterviewStack.io job board, 5,508 for AI Engineer and 2,750 for Business Intelligence Analyst, and their top-30 skill lists overlap by just 18%.

A note on the data: role classifiers aren't perfect, and a minority of each sample sits at the edges of the role's core definition, AI-training and data-labeling contractor postings on the AI Engineer side, and defense or logistics-analyst titles pulled in by the word "intelligence" on the Business Intelligence Analyst side. Neither shifts the skill picture below; both roles' top skill lists stay squarely on-topic.

LLMs, Generative AI, and Retrieval-Augmented Generation (RAG, the technique of grounding a model's answer in a company's own documents) define what an AI Engineer builds, and none of the three crack Business Intelligence Analyst's top-30 list. Its own vocabulary, Data Visualization, Power BI, Tableau, is just as absent on the AI Engineer side. Two roles filed under "works with data," running on almost entirely separate toolkits.

	AI Engineer	Business Intelligence Analyst
Median US base salary (no equity/bonus)	$165,000	$128,100
Active postings	5,508	2,750
Top skill	Python (66.6%)	Data Visualization (66.4%)
Remote + hybrid share	~57%	~43%
Entry-level share	5.3%	5.1%
Skill overlap (Jaccard)	18% shared (pairwise)	18% shared (pairwise)

Key Findings

AI Engineer postings carry a median $165,000 US base salary, a $36,900 (28.8%) premium over Business Intelligence Analyst's $128,100.

AI Engineer posts roughly double the volume: 5,508 active listings versus 2,750 for Business Intelligence Analyst.

Skill overlap (Jaccard similarity, the share of skills the two roles' top-30 lists have in common) is just 18%.

LLMs (39.1%), Generative AI (38.1%), and RAG (37.7%) anchor AI Engineer postings but appear nowhere in Business Intelligence Analyst's top-30 skill list.

SQL runs backward from what the titles suggest: 65.6% of Business Intelligence Analyst postings ask for it, versus just 18.8% of AI Engineer postings.

Entry-level access is nearly identical: 5.3% of AI Engineer postings versus 5.1% of Business Intelligence Analyst postings.

AI Engineer offers more schedule flexibility: ~57% of postings are remote or hybrid, versus ~43% for Business Intelligence Analyst.

Within AI Engineer's own postings, the title's defining skills, LLMs, RAG, Generative AI, pay within about $7,500 of the role's baseline; Distributed Systems and System Design pay $30,000 or more above it.

Same Data, Two Completely Different Jobs

An AI Engineer spends most of the week building the systems that make a language model useful inside a product: retrieval pipelines, model evaluation, inference endpoints, and the prompts that feed the model. Heavy APIs, CI/CD, and Observability signal production software work with an AI-shaped payload, not research. A Business Intelligence Analyst spends the week closer to the business itself: querying and modeling warehouse data, building dashboards, and turning what the numbers say into something a director can act on. Data Governance and Data Quality showing up as exclusive skills signal a role responsible for whether stakeholders trust the numbers, not just display them. One role ships AI systems. The other ships answers.

Which Skills Do AI Engineer and Business Intelligence Analyst Actually Share?

Both roles lean on Python, cloud infrastructure, and machine learning, but not evenly, and one shared skill runs in the opposite direction from what the job titles imply.

Skill	AI Engineer	Business Intelligence Analyst
Python	66.6%	39.4%
SQL	18.8%	65.6%
Automation	34.4%	20.3%
Data Pipelines	18.5%	30.0%
Machine Learning	36.3%	8.4%
Azure	29.5%	15.1%
AWS	32.6%	11.1%
Monitoring	28.8%	11.4%

Python is the closest thing to a true bridge skill between the two roles, but it still skews 1.7x toward AI Engineer.

Python is genuinely mutual, and Automation, Azure, AWS, and Monitoring all clear the shared-skill threshold while still skewing toward AI Engineer's cloud stack. SQL is the outlier, running backward from what the titles suggest: 65.6% of Business Intelligence Analyst postings require it, more than 3.5x AI Engineer's 18.8%. An AI Engineer is more likely to call an API that returns data than to write the query that produces it. If you already know SQL well, it transfers cleanly into Business Intelligence Analyst work; it barely registers on the AI Engineer side.

Where the Two Roles Split Completely

AI Engineer's exclusive cluster is entirely about building AI systems: LLMs (39.1%), Generative AI (38.1%), RAG (37.7%), Prompt Engineering (24.3%), and LangChain (22.0%, an open-source framework for chaining LLM calls into applications), none of which appear in Business Intelligence Analyst's top-30 list. Business Intelligence Analyst's exclusive cluster runs the opposite direction, toward presenting and governing data that's already collected: Data Visualization (66.4%), Power BI (59.5%), Tableau (38.2%), Excel (29.8%), and Data Quality (21.3%). For the full skill breakdown behind each role, see AI Engineer Skills Companies Want in 2026 and Business Intelligence Analyst Skills Companies Want in 2026.

This split is real, but it isn't evidence that Business Intelligence Analysts don't touch AI at all. Job postings only list a skill when it's an explicit requirement, and Machine Learning, the closest thing Business Intelligence Analyst has to an AI-adjacent tag, shows up in just 8.4% of postings. That number measures who's hired to build AI systems, not who uses AI tools to do their job. The Stack Overflow 2025 Developer Survey and JetBrains' 2025 developer ecosystem survey put general AI-tool usage (Copilot, ChatGPT, and similar) at 84% or higher across technical roles, with roughly half using them daily. AI Engineer postings measure who gets paid specifically to build AI; they don't measure who's expected to use it, and that expectation is converging across both roles faster than the postings show. Anyone building the AI Engineer stack from scratch can start with InterviewStack's interactive courses.

Which Role Pays More in 2026?

AI Engineer earns a median $165,000 US base salary versus $128,100 for Business Intelligence Analyst, a $36,900 (28.8%) premium. Both figures are base salary only, drawn from US postings where wage-transparency laws produce consistent disclosure; equity, bonus, and sign-on aren't reported in job postings, so total compensation at top employers runs higher than either number.

The surprise: within AI Engineer's own postings, the skills that define the job title don't buy much of a premium. LLMs (+$7,000 over baseline, n=432), RAG (+$6,000, n=403), Generative AI (+$3,600, n=375), and Prompt Engineering (+$0, n=252) all sit within about $7,500 of the $165,000 baseline. Business Intelligence Analyst shows the identical pattern from the other side: Power BI and Excel, the two tools most associated with the job, pay at or below the role's own baseline.

Skill	Role	Premium vs. role baseline	Sample size
Distributed Systems	AI Engineer	+$35,500	78
Vertex AI (Google Cloud's managed ML platform)	AI Engineer	+$30,600	54
System Design	AI Engineer	+$30,200	77
Power BI	Business Intelligence Analyst	-$3,800	253
Excel	Business Intelligence Analyst	-$13,300	116
Data Science	Business Intelligence Analyst	+$23,700	123
dbt (a SQL-based transformation tool that runs inside the warehouse)	Business Intelligence Analyst	+$21,900	65
Looker	Business Intelligence Analyst	+$19,400	55

US base salary only; the skills that headline each job title pay flat or below baseline, while the real premium sits one layer down in production or pipeline depth.

Knowing how to call an LLM is the entry ticket for AI Engineer; keeping that system running at scale (Distributed Systems, System Design) is what actually moves the number. For Business Intelligence Analyst, the fastest way to close the gap isn't prompting an LLM, it's the data-engineering layer underneath the dashboard: dbt, Looker, and Data Science all clear a $19K-plus premium over baseline.

Who's Hiring, and How Hard Is It to Break In?

AI Engineer posts roughly double the volume of Business Intelligence Analyst, 5,508 active listings versus 2,750, and spreads across a wider geography: Germany, Canada, the UK, and Singapore all crack its top 10 countries. Business Intelligence Analyst's list leans more toward Brazil (9.6%), consistent with Mercado Libre landing as the role's single largest employer in this dataset at a 2.5% share. Worth flagging: some Mercado Libre postings captured under this role are logistics and operations analyst titles rather than classic BI roles, so that employer's share likely overstates its actual BI hiring. Both roles are similarly US-concentrated (33.8% vs. 32.7%) and within a tenth of a point on India's share (11.5% each).

Where the two roles genuinely converge is entry-level access, and it doesn't favor either one: 5.3% of AI Engineer postings and 5.1% of Business Intelligence Analyst postings are explicitly entry-level, and both sit mostly in the mid-level band (56.4% and 64.8%). AI Engineer does skew more senior overall (38.3% senior-or-staff versus 30.0%) and offers meaningfully more remote and hybrid work (~57% versus ~43%).

Match the Work to the Role You'd Actually Do

Choose AI Engineer if you:

Want to build and ship AI systems (LLM pipelines, RAG, agents) rather than analyze existing data, and you're fine with LLM skills being the entry ticket, not the pay lift.
Want more remote and hybrid flexibility: ~57% of postings offer it, versus ~43% for Business Intelligence Analyst.
Don't mind a role that skews more senior (38.3% senior-or-staff), since the market rewards production-systems depth over AI vocabulary alone.

Choose Business Intelligence Analyst if you:

Want to work close to business stakeholders, translating data that's already collected into a decision, rather than building the systems behind it.
Already have strong SQL and visualization chops (Power BI, Tableau) and would rather build the data-engineering layer underneath (dbt, Snowflake, BigQuery) than pivot into AI/ML engineering.
Aren't choosing based on an easier entry bar: at 5.1% entry-level share, it's statistically the same door as AI Engineer, not an easier one.

Either path benefits from realistic practice first. InterviewStack's AI mock interviews cover both tracks, and its Question Bank has focused drills for SQL, LLM system design, and dashboard-to-decision reasoning. If Business Intelligence Analyst is the closer fit, Data Analyst vs Business Intelligence Analyst is a tighter comparison with a much smaller pay gap.

FAQ

Q. How much more does an AI Engineer earn than a Business Intelligence Analyst in 2026?

AI Engineer postings show a median US base salary of $165,000, versus $128,100 for Business Intelligence Analyst, a $36,900 (28.8%) premium. Both figures are base salary only; equity and bonus are not disclosed in job postings.

Q. Do AI Engineer and Business Intelligence Analyst share any skills?

Some, but the overlap is thin. Skill overlap (Jaccard similarity) between the two roles' top-30 skill lists is just 18%. Python is the closest thing to a shared foundation, appearing in 66.6% of AI Engineer postings versus 39.4% of Business Intelligence Analyst postings, though even that skews heavily toward AI Engineer.

Q. Does SQL matter for AI Engineer roles?

Less than the title suggests. SQL appears in just 18.8% of AI Engineer postings, compared to 65.6% of Business Intelligence Analyst postings, where it functions as table stakes. SQL clears the threshold to count as a shared skill by frequency, but the direction of that overlap runs opposite to what the job titles imply.

Q. Do Business Intelligence Analysts need to know AI or machine learning?

Explicit AI-build skills like Generative AI, RAG, and prompt engineering do not appear in Business Intelligence Analyst's top-30 skill list at all; the closest proxy, Machine Learning, shows up in just 8.4% of postings. That measures who is hired to build AI systems, not who uses AI tools day to day. The Stack Overflow 2025 Developer Survey puts general AI-tool usage at 84% or higher across technical roles, so ambient use, like Power BI Copilot or ChatGPT for query drafting, is a separate and much larger number.

Q. Which role has more open positions in 2026?

AI Engineer, by a wide margin. This dataset shows 5,508 active AI Engineer postings versus 2,750 for Business Intelligence Analyst, roughly double.

Q. Which skills actually pay a premium within AI Engineer postings?

Not the ones in the job title. LLMs, RAG, and Generative AI, the skills that literally define the role, sit within about $7,500 of the role's $165,000 baseline. The larger premiums show up one layer down, in skills like Distributed Systems (plus $35,500) and System Design (plus $30,200).

Q. Is Business Intelligence Analyst an entry-level friendly role?

About as much as AI Engineer is, and no more. Entry-level share is nearly identical for both roles: 5.1% for Business Intelligence Analyst versus 5.3% for AI Engineer. Neither role is an easy first job; both sit mostly in the mid-level band.

The Premium Sits in the Depth, Not the Title

AI Engineer and Business Intelligence Analyst share a market, both count as "data" work at most companies, but almost nothing else: an 18% skill overlap, zero AI-specific crossover, and a $36,900 pay gap that traces to production-systems depth on one side and data-pipeline depth on the other, not to the word AI in a job title. If you're already in one of these roles, raising your ceiling doesn't necessarily mean switching roles. Browse current AI Engineer or Business Intelligence Analyst openings and see which vocabulary you already speak.

Data Analyst Business Intelligence Interview: Two Tables, Two Truths

Gnana — Thu, 23 Jul 2026 02:23:16 +0000

The Data Analyst Business Intelligence and Reporting Infrastructure Interview Rewards Numbers You Can Defend

You are a mid-level Data Analyst, moments into a 30-minute interview, and the scenario has already told you the dashboards disagree. Not "might disagree." Do disagree, today, in production, and finance and operations are both tired of it. The interviewer isn't handing you a blank canvas; they're handing you a mess and watching how you clean it up.

This walkthrough is built on one real AI-interview blueprint (the same package structure our AI interviewer generates and scores against), covering a Data Analyst interview on Business Intelligence, Reporting, and Dashboards at the mid-level (2-5 years). The scoring is 100 points across four weighted dimensions, and most of it is decided before a single chart gets designed.

Key Findings

Interviewer Objectives Alignment and Level-Specific Expectations each carry 30 of 100 points, 60 total, and both are graded on how you frame the problem, not how many KPIs you list.

The Semantic layer and dashboard design phase (minutes 7-20) packs 6 separate checklist items into 13 minutes.

4 source tables are given, and 2 of them (a daily snapshot and a transaction log) are designed to disagree with each other in specific, explainable ways.

The interview names a concrete failure case: a drill-down view that stalls at 20 seconds after filtering by country and plan tier.

Only 7 of the 30 minutes (0-7) are allotted to establishing trust, audience, and performance as requirements before any dashboard widget gets designed.

Technical Proficiency and Communication & Problem Solving split the remaining 40 points, both scored on structured reasoning, not just correct conclusions.

Just 3 minutes (27-30) are left for prioritization and stakeholder sign-off at the close.

Framing and level-specific judgment together outweigh raw technical execution, 60 points to 40.

The Scenario: A Dashboard Built on Disagreement

The interview question

You support a consumer subscription product with executives, finance, and operations teams all using dashboard reporting. The company currently has inconsistent metric definitions across Tableau and Looker dashboards, slow dashboard load times during business hours, and frequent questions about why numbers differ between weekly reports.

A new leadership request asks for a single "Subscription Health" dashboard that will be used in weekly business reviews and by managers during the week for drill-down analysis. The core source tables available are:

-- daily grain subscription status snapshot
subscription_daily_status (
  snapshot_date DATE,
  user_id STRING,
  subscription_id STRING,
  plan_tier STRING,
  billing_country STRING,
  subscription_status STRING, -- active, paused, canceled, trial
  is_trial BOOLEAN,
  mrr_usd NUMERIC,
  start_date DATE,
  cancel_date DATE
)

-- event-level billing activity
billing_transactions (
  transaction_id STRING,
  user_id STRING,
  subscription_id STRING,
  transaction_ts TIMESTAMP,
  transaction_type STRING, -- renewal, refund, initial_purchase, retry_success, chargeback
  amount_usd NUMERIC,
  currency STRING,
  transaction_status STRING -- success, failed
)

-- support tickets
support_tickets (
  ticket_id STRING,
  user_id STRING,
  created_ts TIMESTAMP,
  issue_type STRING,
  resolution_hours NUMERIC,
  csat_score NUMERIC
)

-- product engagement summary at user-day grain
user_engagement_daily (
  event_date DATE,
  user_id STRING,
  sessions INT64,
  minutes_watched INT64,
  devices_used INT64
)

How would you design and deliver this Subscription Health dashboard so that business users can trust the numbers, answer common drill-down questions, and use it interactively without performance issues?

The interviewer's objectives are explicit about what they are and are not testing: can you translate a vague leadership request into precise metrics, propose a practical semantic layer, make sound dashboard design calls, and reason about refresh, trust, and performance trade-offs at a mid-level scope. Nobody is grading your Tableau chart-type vocabulary. They're grading whether the dashboard you describe would actually survive contact with finance asking "why doesn't this match last week's number."

Turn 1: Metrics Without Definitions

Interviewer: "How would you define the core metrics on this dashboard so finance and operations stop using conflicting numbers?"

COMMON MISTAKEA common answer is to rattle off metric names, active subscriptions, churn, MRR, refund rate, without stating the underlying logic for any of them. That misses the checklist item that KPIs need enough precision to reveal logic choices, and it never actually explains why finance and operations disagreed in the first place.
STRONGER MOVEPick one or two headline metrics and say the definition out loud: active subscription equals subscription_status = active as of snapshot_date, excluding paused, and net MRR equals mrr_usd summed over active rows minus same-day refunds pulled from billing_transactions. Naming the filter and join logic, not just the metric label, is what proves you can end the disagreement.

Turn 2: Where Logic Lives

Interviewer: "What would you put in the semantic or metrics layer versus calculating directly in a dashboard tool?"

COMMON MISTAKEAnother common answer treats this as a style choice and puts most of the logic as calculated fields inside Tableau and Looker, since it's faster to iterate on. That's the opposite of the top Phase 2 checklist item, a source-of-truth metric layer instead of ad hoc dashboard calculations, and it concedes both Level-Specific Expectations and Technical Proficiency points at once.
STRONGER MOVEPush anything that must stay consistent across tools, active subscription, net MRR, churn, into a modeled layer computed once upstream. Reserve the dashboard tool for filters, drill paths, and formatting only. That single design choice is what stops Tableau and Looker from drifting apart again.

Turn 3: The 20-Second Filter

Interviewer: "Suppose users complain that one drill-down view takes 20 seconds to load after filtering by country and plan tier. How would you investigate and improve it?"

COMMON MISTAKEA frequent reflex is jumping straight to "add a cache" or "add an index" without inspecting what the query is actually doing. That skips the checklist's explicit investigation step, query inspection, filter patterns, join logic, dashboard computation review, and costs Technical Proficiency points for prescribing a fix before diagnosing the problem.
STRONGER MOVEWork the layers in order: check whether the view is querying raw event-grain tables instead of a pre-aggregated one, look for a high-cardinality join happening before the country and plan tier filters apply, and confirm the aggregation isn't running inside the dashboard tool itself. Naming partition pruning by snapshot_date and a pre-aggregated grain shows you understand the fix, not just the symptom.

Turn 4: Two Truths, One Metric

Interviewer: "If cancellation counts from the status snapshot do not match counts inferred from transactions, how would you diagnose and communicate the discrepancy?"

COMMON MISTAKEA common response treats the mismatch as a bug that needs fixing so the two numbers finally agree, instead of recognizing that a snapshot table and an event log are structurally different by design. That misses the level-specific expectation to recognize snapshot-versus-event logic differences and costs Communication points for not explaining the caveat to stakeholders.
STRONGER MOVEExplain the structural reason first: a cancellation can post as a transaction mid-day before the next snapshot reflects it, or a chargeback can show up in transactions with no corresponding status change. Then propose a reconciliation check and a documented definition for each number so finance and operations know which one answers which question.

Why Isn't Reading This Enough?

Every mistake above is easy to spot on the page. The wrong turn is already labeled red; the fix is already labeled green. That's not the skill being tested. The skill being tested is catching your own version of that mistake in real time, mid-sentence, while an interviewer is waiting on your answer and the clock has already burned through Phase 1. Reading a mismatch diagnosis is not the same as building one under pressure with no script. That gap only closes with reps.

What Does a Strong 30-Minute Interview Actually Look Like?

Trust and framing get 7 minutes up front; performance and reliability get their own dedicated phase before the close.

This is the blueprint a strong candidate hits, phase by phase, and it's the exact thing our AI interviewer tracks you against in real time as you talk.

Blueprinta strong 30-minute interview, phase by phase1Problem framing and success criteria 0-7

✓Asks who the primary users are and distinguishes executive review from manager self-serve usage
✓Calls out the need for standardized metric definitions because current dashboards disagree
✓Identifies likely key outcomes such as retention/churn, active subscriptions, revenue, payment health, and support/engagement context
✓Mentions trust, drill-down usability, and performance as first-class requirements rather than afterthoughts

2Semantic layer and dashboard design 7-20

✓Proposes a source-of-truth metric layer or curated reporting tables rather than relying on ad hoc dashboard calculations across tools
✓Distinguishes which tables are best for point-in-time subscription metrics versus transaction-based revenue/payment metrics
✓Defines example KPIs with enough precision to reveal logic choices, such as active subscriptions, net MRR, churned subscriptions, renewal success rate, refund rate, trial conversion, or support burden
✓Outlines a dashboard hierarchy such as overview KPIs, trend section, segmentation filters, and drill-down/detail table
✓Describes meaningful drill-down dimensions like plan tier, billing country, acquisition cohort or status, while avoiding overly cluttered layouts
✓Recognizes edge cases like paused subscriptions, refunds, chargebacks, retries, and differences between daily snapshot counts and transaction events

3Performance, refresh, and reliability 20-27

✓Suggests pre-aggregated or modeled tables for common dashboard grains instead of hitting large event tables directly for every interaction
✓Mentions relevant optimization concepts such as partition pruning by date, clustering/filter-friendly keys, reducing high-cardinality joins, or extracting expensive calculations upstream
✓Proposes different refresh cadences for executive reporting versus operational monitoring if needed
✓Describes basic validation checks such as reconciliation between source and reporting tables, metric definition docs, or alerts on anomalous changes
✓Explains how they would investigate a slow view using query inspection, filter patterns, join logic, and dashboard computation review

4Communication and prioritization wrap-up 27-30

✓Prioritizes an MVP with a small set of trusted KPIs and high-value drill-downs before expanding breadth
✓Identifies stakeholders needed for sign-off, such as finance for revenue logic and operations for cancellation/support definitions
✓Communicates trade-offs clearly, including what would be delivered now versus later

Practice the Full Interview Before It's Live

You've now seen four ways this specific interview gets away from a well-prepared candidate. The fastest way to make sure none of them get away from you is to run the scenario yourself, live, against an interviewer that follows this exact blueprint and scores you against these exact checklist items. Start the AI mock interview on business intelligence and reporting infrastructure and get feedback on your framing, your metric definitions, and your handling of the snapshot-versus-transaction trap before an interviewer hands it to you for real.

If you'd rather drill the underlying concepts first, semantic layer design, refresh trade-offs, dashboard performance, the business intelligence and reporting infrastructure question bank breaks the topic into focused practice questions, and the Data Analyst preparation guide rounds out the rest of the role.

FAQ

Q. What does a Data Analyst interview on business intelligence and reporting actually test?

It tests whether you can turn a vague "build me a dashboard" request into a trustworthy reporting system: precise metric definitions, a source-of-truth semantic layer, sensible dashboard architecture, and reasoning about refresh cadence, performance, and data-quality edge cases, not just tool familiarity with Tableau or Looker.

Q. How many rubric points come from how you frame the problem versus the technical build?

Interviewer Objectives Alignment and Level-Specific Expectations each carry 30 of the 100 total points, 60 in total, and both hinge on framing: identifying stakeholders, standardizing metric definitions, and making practical trade-offs. Technical Proficiency and Communication & Problem Solving split the remaining 40.

Q. What is a semantic or metrics layer, and why does it matter for this interview?

A semantic (or metrics) layer is a curated, modeled set of tables or definitions that compute business logic once, upstream of any dashboard tool, so every report pulls the same number for "active subscriptions" or "net MRR." The interview's expected checklist explicitly rewards proposing this over ad hoc calculations built separately in Tableau and Looker.

Q. Why would a subscription count from a daily snapshot table differ from one built off transaction events?

A daily snapshot table records subscription state as of a point in time, while a transaction log records discrete billing events. A cancellation can post as a transaction mid-day before the next snapshot reflects it, or a chargeback can appear in transactions with no status change at all, so the two can legitimately disagree without either being wrong.

Q. How should I investigate a dashboard drill-down that takes 20 seconds to load?

Work through the layers in order rather than guessing at a fix: check whether the dashboard queries raw event-grain tables instead of a pre-aggregated table, look for high-cardinality joins happening before filters are applied, and confirm whether expensive aggregation is running inside the dashboard tool instead of the warehouse.

Q. How long is this mock interview and how is it scored?

The full interview runs 30 minutes across four phases (problem framing, semantic layer and dashboard design, performance and reliability, and prioritization), scored out of 100 points across four weighted dimensions, mirroring what a mid-level Data Analyst would face in a real BI-focused interview.

Q. Where can I practice this exact interview format?

You can run the full AI mock interview on this scenario, or drill focused practice questions on business intelligence and reporting infrastructure in the question bank, both linked in this post.

Two Numbers Can Both Be Right

The trap in this interview was never a bad query or a missing chart. It was mistaking two structurally different data sources for one that had simply broken. Get comfortable saying "these numbers disagree, and here is exactly why" instead of chasing false agreement, and you're already ahead of most candidates who walk in.

Solutions Architect AI Demand Splits 17x by Employer in 2026

Gnana — Tue, 14 Jul 2026 17:56:20 +0000

The AI Divide Runs Through the Employer, Not the Job Title

Two people can hold the exact same job title, Solutions Architect, at two different companies, and one of them is doing meaningfully AI-native work while the other almost never touches it. That is not a hunch. Across active Solutions Architect postings on the InterviewStack.io job board, 5,083 listings over a rolling 90-day window with AI-related terms tagged directly from the posting text, technology companies require AI skills in 37.5% of their Solutions Architect postings. IT services firms, hiring for a title that reads identically on a resume, require it in just 2.2%. That is roughly a 17x gap, and it is far larger than the gap between any two seniority levels in the same dataset.

That is the story most "AI is changing this role" posts miss: they look for a shift over time or over seniority, and Solutions Architect does show both. But the sharper cut here is horizontal, across employer type, not vertical, across career stage. If you're deciding where to point your job search, which company is hiring matters more than which level you're applying for.

A scope note before the numbers: this dataset matches on the Solutions Architect title family, and a spot-check of sample listings shows it also pulls in adjacent titles, Enterprise Architect, Business Architect, and Solutions Consultant among them, alongside the core "Solution(s) Architect" title. That widens the picture to the broader solutions/enterprise-architecture family rather than isolating the individual-contributor, AWS/Azure-style title alone, so treat the percentages below as describing that wider family. Separately, the IT services figure below is built on just 3 AI-requiring postings out of 138 total for that industry, small enough that the exact multiple should be read as directional rather than a precise measurement.

Key Findings

26.8% of Solutions Architect postings (1,360 of 5,083) explicitly require new-wave generative AI skills; 35.8% (1,821) require some form of AI, generative or traditional machine learning.

Technology companies require AI in 37.5% of their Solutions Architect postings (392 of 1,046) versus 2.2% at IT services firms (3 of 138), a roughly 17x gap.

Traditional Machine Learning is the single top AI-related skill at 19.9% (1,011 postings); among new-wave skills, AI Agents leads at 16.6% (844), ahead of Generative AI (14.6%) and LLMs (9.1%).

AI-requiring postings carry a $14,050 US median base salary premium: $184,000 (n=406) versus $169,950 without AI (n=715).

Seniority barely moves the AI rate: 25.9% at mid-level, 27.6% at senior, 23.4% at staff, a narrow band compared to the employer-type gap.

Senior-level postings dominate the role at 78.5% of all listings; entry and junior combined make up just 3.2%.

The US is the largest single market at 38.1% of postings, with a 29.5% AI rate; India follows at 9.0% share and a 22.6% AI rate.

What Solutions Architect Work Looked Like Before Generative AI

Three or four years ago, a Solutions Architect's AI-adjacent work was almost entirely translation work: taking a data science team's model output and wiring it into a customer-facing system, or evaluating a vendor's "AI-powered" analytics add-on for a client's stack. Machine learning and BI integration were already part of the job, which is exactly why traditional Machine Learning still shows up in nearly a fifth of today's postings, it's a holdover skill, not a new one. What has changed since 2023 is the object being architected. Instead of wiring a static prediction into a dashboard, architects are now asked to design systems where an LLM or an agent makes decisions, calls tools, and acts autonomously inside a customer's environment, and to do that securely, at scale, and inside existing compliance boundaries.

That shift shows up clearly in enterprise-architecture leadership research: 92% of EA leaders now name AI or agentic architecture their top trend for 2026, and 72% cite AI or data-architecture skill as the biggest capability gap on their teams (Avolution, Enterprise Architecture in 2026). Deloitte's enterprise AI research adds a second, related data point: 76% of enterprise AI use cases today are deployed via third-party, off-the-shelf products rather than custom-built models (Deloitte, State of AI in the Enterprise). That second stat matters specifically for this role: a Solutions Architect evaluating and integrating a vendor's AI product is doing AI-adjacent work even when the job posting never uses the words "generative AI," which is one reason the explicit percentages below likely undercount the real footprint of AI in this job.

How Many Solutions Architect Jobs Actually Require AI?

Job-posting language only captures one layer of AI adoption: the explicit ask to build, deploy, or govern an AI system. It misses the much larger ambient layer, using AI tools as part of daily work, that most employers now assume without writing into the requisition, the same way "internet access" never appeared as a listed skill in a 2005 job ad.

26.8% of postings explicitly require new-wave generative AI skills; 21.7% require traditional machine learning; the two overlap in 12.7% of postings, and 35.8% require some form of AI overall.

Layer one, the explicit build-AI ask, sits at 26.8% (new-wave) to 35.8% (any AI). Layer two, ambient use, is far higher. General developer-tool surveys put AI-tool usage at 84% (up from 76% the year before) and daily use at 51% among professional developers (Stack Overflow 2025 Developer Survey), with JetBrains reporting 85% of developers regularly using AI tools for coding and 62% relying on at least one AI coding assistant, agent, or editor (JetBrains State of Developer Ecosystem 2025). Architects sit a layer above hands-on coding, so those exact figures don't transfer one-to-one, but the EA-specific numbers above (92% naming AI/agentic architecture their top trend, 72% naming it their top skill gap) point the same direction: the 26.8% figure measures who is hired to explicitly build or govern AI systems, not how many architects are expected to understand and use AI as part of the job. Virtually all of them are.

The Real Fault Line Is the Employer, Not the Job Title

If explicit AI demand were mostly a function of seniority, you'd expect a steady climb from entry-level to staff. It isn't. The AI requirement rate moves in a narrow band across every level with a meaningful sample: 25.9% at mid-level (n=640), 27.6% at senior (n=3,990, the level that makes up 78.5% of all postings), and 23.4% at staff (n=290). Junior sits lower at 14.3% (n=133), and entry-level shows 20% but on a tiny sample of 30 postings, worth flagging as directional rather than reading as a trend.

Outside the small entry-level sample, AI adoption holds between roughly 23% and 28% from mid-level through staff, a narrow band compared to the gap seen across employer types below.

Now compare that to industry. Technology companies require AI in 37.5% of Solutions Architect postings (392 of 1,046); software companies, 28.9% (200 of 692). Healthcare sits at 21.1% (n=152) and finance at 20.0% (n=110). Consulting (15.5%, n=226) and fintech (13.9%, n=137) trail further. IT services firms, the traditional systems-integration and staffing-adjacent end of the market, sit at just 2.2% (3 of 138 postings).

Technology and software employers require AI in Solutions Architect postings at roughly 3-17x the rate of IT services firms, a bigger swing than anything seniority produces.

Put those two charts side by side and the shape of the role becomes clear: a Solutions Architect job at a technology or software company is now substantially an AI-systems job, roughly one in three to one in four postings say so outright, while the same title at an IT services firm is still overwhelmingly a traditional integration job. Neither is "the real" Solutions Architect role; they're two different jobs sharing one title, and the company you target tells you more about which one you'll get than your years of experience does.

Which AI Skills Are Solutions Architects Actually Hired to Build?

Traditional Machine Learning is still the single most common AI-related skill in Solutions Architect postings at 19.9% (1,011 of 5,083), a legacy of years of BI and predictive-analytics integration work that predates the current wave. Among skills specific to the generative AI era, AI Agents leads at 16.6% (844), ahead of Generative AI at 14.6% (741) and LLMs at 9.1% (465).

AI Agents outranking Generative AI and LLMs signals architects are increasingly hired to design autonomous, tool-calling systems, not just add a conversational feature to an existing product.

That ranking, AI Agents ahead of plain Generative AI, is the clearest signal in the skills data. Companies aren't mostly asking Solutions Architects to bolt a chatbot onto a product; they're asking them to design systems where an agent plans, calls tools, and acts with some autonomy inside a customer's environment, which is a harder and more architecturally consequential problem than adding an LLM endpoint. Further down the list, RAG (5.6%), MLOps (5.7%), and Prompt Engineering (4.4%) all sit in the single digits, supporting infrastructure around the two headline concepts rather than standalone requirements on their own.

The AI Salary Premium Is Real, but It's Not Huge

Among US postings with disclosed base salary (equity, bonus, and sign-on are not captured in job-board data and are not reflected in either figure below), Solutions Architect roles that require new-wave AI skills show a $184,000 median base, compared with $169,950 for roles that don't, a $14,050 premium. Both samples clear a comfortable size, 406 AI-requiring postings and 715 without, so this is a stable read rather than a small-sample guess.

A $14,050 US base-salary premium for AI-requiring Solutions Architect postings, real but modest next to the 17x gap in who even asks for AI in the first place.

Read against the employer-type gap above, that premium is worth putting in context: $14,050 is a real number, but it's a much smaller signal than the difference between working at a company that requires AI 37.5% of the time versus one that requires it 2.2% of the time. The bigger financial decision for most architects isn't "should you learn AI for the raise," it's "which kind of employer you're applying to."

Who's Building the AI Stack

Ranked by the volume of AI-requiring Solutions Architect postings, Amazon leads by a wide margin (206 AI-requiring postings of 433 total, 47.6%), followed by NVIDIA (137 of 243 combined listings, 56.4%) and Adobe (51 of 76 combined listings, 67.1%). Databricks and MongoDB each show 23 AI-requiring postings, though MongoDB's is a far higher share of its total volume (23 of 26, 88.5%, versus Databricks' 23 of 102, 22.5%). Salesforce, Zip, SAP, Genesys, and Braze round out the top ten.

Company	AI-requiring postings	Total postings	AI rate
Amazon	206	433	47.6%
NVIDIA	137	243	56.4%
Adobe	51	76	67.1%
Databricks	23	102	22.5%
MongoDB	23	26	88.5%
Salesforce	17	17	100%
Zip	16	17	94.1%
SAP	16	41	39.0%
Genesys	15	17	88.2%
Braze	12	13	92.3%

Geographically, the US is the largest single market at 38.1% of postings with a 29.5% AI rate, and India follows at 9.0% of postings with a 22.6% rate. The UK's smaller share (5.8%) carries a slightly higher AI rate than the US at 31.6%, and Mexico is a notable, if small-sample, outlier at 57.6% (n=59), worth watching rather than treating as an established trend.

How to Use This in Your Job Search

If you're targeting the AI-heavy end of this role, start by mapping which companies actually ask for it: browse current Solutions Architect openings that require AI Agents or Generative AI rather than assuming every "Solutions Architect" posting is equivalent. The employer-type gap above means the title alone won't tell you which job you're applying for.

To close the gap between where you are and what these postings ask for, practice explaining agentic system design out loud with AI mock interviews, the kind of scenario question ("design a system where an agent can call three internal tools safely") that's becoming standard at technology and software employers. If your Machine Learning fundamentals are rusty or you've never worked with RAG or vector databases, the question bank is the fastest way to drill the concepts by topic, and the interactive courses are a better fit if you need to build foundational fluency in ML or generative AI systems from scratch rather than just practice interview answers. Once you've mapped the target companies and shored up the gaps, the full Solutions Architect board is where to track new postings as they go live.

FAQ

Q. What percentage of Solutions Architect jobs require AI skills in 2026?

26.8% of active Solutions Architect postings (1,360 of 5,083 analyzed) explicitly require new-wave generative AI skills. Including traditional machine learning and deep learning, the any-AI figure rises to 35.8% (1,821 postings). That is the explicit hiring ask; it does not capture how many architects already use AI tools day to day, which is much higher.

Q. Does AI adoption vary by the type of company hiring a Solutions Architect?

Yes, dramatically. Technology companies require AI in 37.5% of their Solutions Architect postings (392 of 1,046) and software companies in 28.9% (200 of 692). IT services firms sit at just 2.2% (3 of 138), roughly a 17x gap from technology. The job title is identical across all three; the AI content of the work is not. Note the IT services figure rests on only 3 AI-requiring postings, so read the exact multiple as directional.

Q. Does seniority determine whether a Solutions Architect job requires AI?

Not much, and that is itself notable. The AI requirement rate holds in a narrow band across the levels with real sample size: 25.9% at mid-level (n=640), 27.6% at senior (n=3,990), and 23.4% at staff (n=290). Compare that to the 17x swing by employer type: which company you target predicts AI exposure far better than which level you're applying for.

Q. What is the top AI skill required for Solutions Architect roles?

Traditional Machine Learning is the single most-requested AI-related skill at 19.9% of postings (1,011 of 5,083), reflecting years of BI and predictive-analytics work already baked into the role. Among new-wave generative AI skills specifically, AI Agents leads at 16.6% (844 postings), ahead of Generative AI (14.6%) and LLMs (9.1%), which signals companies are hiring architects to design agentic systems, not just add a chatbot to an existing product.

Q. Is there a salary premium for Solutions Architects with AI skills?

A real but modest one. Among US postings with disclosed salary, those requiring new-wave AI skills show a $184,000 median base (n=406), compared with $169,950 for those that don't (n=715), a $14,050 premium. Both samples are well above the reporting floor, so this is a stable read, not a directional guess. Equity, bonus, and sign-on are not included in either figure.

Q. Which companies are hiring the most AI-focused Solutions Architects?

By volume of AI-requiring postings: Amazon (206 of 433 total postings), NVIDIA (137 of 243), Adobe (51 of 76), Databricks and MongoDB (23 each), and Salesforce, Zip, SAP, Genesys, and Braze rounding out the top ten. Amazon and NVIDIA combine large volume with high AI rates; MongoDB and Salesforce show smaller but nearly saturated AI requirements.

Q. Do Solutions Architects need AI skills even if a job posting doesn't mention them?

Almost certainly, in practice. Enterprise-architecture leadership surveys show 92% of EA leaders now name AI or agentic architecture their top trend for 2026, and 72% cite AI or data-architecture skill as their biggest capability gap. Separately, general developer-tool surveys put AI-tool usage at 84-85% and daily use above 50%. The posting language lags what the job actually requires.

Read the Employer, Not Just the Title

"Solutions Architect" in 2026 isn't one job with an AI layer sprinkled on top, it's two jobs wearing the same title, and which one you get depends far more on your employer than your resume. If you want the AI-heavy version, technology and software companies are where the postings, and the skills that get you hired for them, actually line up.

SDET Skills in 2026: Automation Is the Baseline, Not the Premium

Gnana — Fri, 10 Jul 2026 04:35:17 +0000

Automation Is Everywhere in SDET Postings. It Isn't What Pays.

Automation is the one skill nearly every Software Development Engineer in Test (SDET) posting names outright, appearing in 87.0% of listings, one of the highest table-stakes rates we've measured across roles in this series. If you're wondering whether you need it, the answer is: obviously, everyone already has it on their resume. That's exactly why it barely moves your offer.

We looked at every active SDET posting on the InterviewStack.io job board as of July 2026, 1,027 listings, with skills extracted from descriptions and synonyms collapsed. The pattern that falls out is a role where the entry ticket (automation, plus a testing framework) is nearly universal and cheap, while the pay ladder runs through a different set of skills entirely: cloud, CI/CD infrastructure, and pipeline ownership.

Key Findings

Automation appears in 87.0% of SDET postings (894 of 1,027), the only skill to cross the 50% table-stakes threshold.

Median US base salary is $130,000 across 181 postings with US salary disclosed.

AWS carries the largest premium of any skill: $171,000 median (+$41,000 over baseline), though only 16.3% of postings ask for it.

CI/CD, Jenkins, Docker, and automated testing each add $25,000 to $29,000 over baseline; automation itself adds just $6,500.

Java and Selenium co-occur at 2.26x the rate chance would predict, the strongest skill pairing in the dataset.

Only 2.4% of postings are entry-level (97.6% are mid-level or above), one of the more experience-gated roles in this dataset.

Onsite remains the dominant work mode at 54.8%; only 15.0% of postings are tagged remote.

India accounts for 22.5% of postings, the second-largest country behind the US at 34.8%.

A note on that 87% automation figure. Our classifier matches on title and description keywords, and "automation" also shows up in industrial and controls-engineering job titles, electrical automation, manufacturing automation, controls specialists, that have nothing to do with software testing. A random sample of postings behind this dataset turned up a handful of exactly those titles. That means 87.0% likely overstates pure software test-automation demand somewhat. It doesn't change the direction of the finding: the software-specific signal (Selenium, Playwright, CI/CD, Jenkins, test automation as a named discipline) is consistent and unaffected by that overlap, and it's what the rest of this post is built on.

Which SDET Skills Actually Pay More Than the Baseline?

Salary figures below cover US postings only (where wage-transparency laws produce consistent disclosure) so they're directly comparable. The numbers are base salary: equity, bonus, and sign-on aren't disclosed in postings and aren't in this dataset, so total compensation at large tech and aerospace employers runs meaningfully higher than what we report here.

The median US base salary across all SDET postings is $130,000 (n=181). Against that baseline, the highest-paying skills aren't the testing frameworks, they're the cloud and pipeline infrastructure a smaller share of postings actually ask for.

Median US base salary in USD for postings that mention each skill, among US SDET postings with structured salary data.

Skill	Median US base salary	Premium over baseline	Sample size
AWS	$171,000	+$41,000	27
Automated Testing	$159,000	+$29,000	31
CI/CD	$158,300	+$28,300	72
Jenkins	$157,100	+$27,100	48
Docker	$155,000	+$25,000	41
Scalability	$151,600	+$21,600	34
JavaScript	$145,800	+$15,800	28
Python	$145,000	+$15,000	85
Git	$144,000	+$14,000	30
Playwright	$141,900	+$11,900	26

Now compare that to the skills nearly every SDET already has on their resume. Automation itself, the skill in 87% of postings, sits at $136,500 (+$6,500, n=160). Agile is $136,000 (+$6,000, n=49). Selenium, still the most-named testing framework by raw count, is $134,300 (+$4,300, n=35). Quality Assurance as a named skill sits exactly at the $130,000 baseline (+$0, n=44).

The shape is consistent: skills that show up in almost every posting have flat salary distributions because they're the price of entry, not a differentiator. AWS, at just 16.3% of postings, is rare enough that companies pay for it: $41,000 above baseline, a 31.5% premium, the single largest gap in this dataset. Browse SDET openings that ask for AWS if that's the direction you want to take.

What Does the SDET Skill Stack Actually Look Like?

Group individual skills into families and two clusters effectively tie for the top spot. A testing-and-pipeline tooling cluster, CI/CD, test automation, Selenium, Jenkins, Playwright, API testing, and quality assurance, touches 90.6% of postings. A general developer-infrastructure cluster, Git, Docker, GitHub, Jira, monitoring, Kubernetes, touches 90.4%.

Share of SDET postings that ask for at least one skill in each family. A posting that mentions both Jenkins and GitHub Actions counts once under its tooling family.

That second number is misleading if you read it as a broad infrastructure requirement. Automation alone drives nearly all of that 90.4%: strip it out and the rest of that family, Git, Docker, GitHub, Jira, monitoring, Kubernetes, each show up individually in just 9-15% of postings. The 90% headline is really one dominant skill plus a long, thin tail.

Below those two testing clusters: Coding Languages at 59.6% (Python 40.8%, Java 27.4%, JavaScript 19.2%, TypeScript 14.0%, C# 9.1%), Process & Methodology at 38.0% (mostly Agile and Scrum), Querying & SQL at 20.9% (almost entirely SQL itself), and Cloud Platforms at 19.6% (AWS, Azure, Google Cloud). The coding-language mix confirms SDET is still a Python-and-Java role first, with JavaScript and TypeScript trailing behind for teams testing web-heavy products.

What Are the Three Tiers of SDET Skills in 2026?

Individual skills split cleanly into three bands once you rank them by how many postings mention them.

Top individual skills in SDET postings, by share of listings that mention them. Skills above 50% are table stakes; 20-50% are common; 5-20% are differentiators.

Table stakes (50%+): Automation, 87.0%, is the only skill in the entire dataset to clear this line. Nothing else, not a specific testing framework, not a specific language, comes close.

Common (20-50%): CI/CD (42.4%), Python (40.8%), Test Automation (39.3%), Agile (29.5%), Java (27.4%), Selenium (25.7%), and Jenkins (23.1%). This is the practical floor for a competitive SDET resume: a pipeline tool, a language, a named test-automation discipline, and one specific framework.

Differentiators (5-20%): A wide band of 42 skills, from JavaScript (19.2%) and Playwright (18.8%) down to Load Testing (6.8%). The most useful ones to know by name: SQL (18.1%), Quality Assurance as an explicit label (16.6%), AWS (16.3%), API Testing (16.1%), Automated Testing (16.0%), Git and Docker (15.5% each), GitHub (15.3%), Debugging (14.2%), TypeScript (14.0%), Kubernetes (10.9%), Cypress (9.6%), and C# (9.1%). None of these are required to get past a resume filter, but this is exactly the band the salary data says companies pay extra for.

Java and Selenium Still Define the Enterprise Test Stack

Computing every two-skill co-occurrence among the top 25 skills surfaces two distinct stacks hiding inside the same job title.

The strongest single pairing in the dataset is Java + Selenium, at a co-occurrence lift of 2.26 (163 postings, 15.9%): postings that ask for Java are more than twice as likely as chance would predict to also ask for Selenium. That's the classic enterprise QA-automation combination, and it's still the dominant enterprise pattern in 2026, not a legacy holdover.

The second cluster is built around pipeline ownership: CI/CD + Jenkins (lift 2.05, 20.1%), CI/CD + Playwright (lift 1.97, 15.7%), Jenkins + Test Automation (lift 1.79, 16.3%), and CI/CD + Java (lift 1.68, 19.5%). Playwright (Microsoft's browser-automation framework, positioned as Selenium's modern successor) shows up paired with CI/CD more strongly than it shows up alone, meaning postings that mention it are usually describing an engineer who owns the pipeline, not just the test scripts.

That distinction lines up directly with the salary data above. Java + Selenium is the resume that gets you an interview. CI/CD + Jenkins or CI/CD + Playwright is the resume that gets you the $25K-28K premium, because it signals you can build and maintain the pipeline the tests run in, not just write the tests themselves.

AI Testing Tools Aren't in the Job Post, But They're in the Workflow

Only about 9.6% of SDET postings even touch the Machine Learning & AI category (99 of 1,027), and none of those resolve to a specific named AI skill, no LLM, no prompt engineering, no MLOps line item anywhere in the top 50 skills. Treat 9.6% as a loose ceiling, not a clean measurement: these are postings where AI shows up adjacently, as a system under test or a testing target, not as a hard requirement to build AI systems. That's the expected reading for a testing role. SDETs are hired to verify what other teams build, not to build AI models themselves.

But job postings only capture what's stated as a requirement, not what's assumed. The testing industry runs its own annual survey of that gap, and it tells a different story. Capgemini's World Quality Report 2025-26 found 89% of quality-engineering organizations are already piloting or running GenAI-augmented QE workflows (37% in production, 52% still piloting). Developers broadly report similar numbers: 84% use or plan to use AI tools and 51% use them daily, according to the Stack Overflow 2025 Developer Survey; 85% of developers report using AI regularly per JetBrains' 2025 ecosystem survey.

Read together, the honest framing is this: postings screen for Selenium, Playwright, CI/CD, and Python because those are what a hiring manager can verify on a resume. AI-assisted test generation, self-healing automation, and Copilot-style completion inside test scripts are increasingly the water SDETs swim in day to day, whether or not the posting spells it out.

Who's Getting Hired, and at What Level?

Entry-level SDET postings are scarce.

Seniority distribution of SDET postings, inferred from title keywords.

Entry: 2.4% (25 postings)
Mid-level: 61.8% (635)
Senior: 21.9% (225) (senior SDET openings)
Staff: 13.8% (142)

Combined, 97.6% of SDET postings are mid-level or above, roles that typically assume you already know how to design and own an automation framework before you walk in the door. There's no meaningful junior on-ramp here the way there is for some engineering roles; companies expect candidates to arrive with production automation experience, usually from a QA analyst, manual-testing, or junior developer role that built up automation skills on the side.

Where Are SDET Jobs, and Are They Remote?

SDET is one of the more globally distributed roles we've analyzed.

Top countries by share of SDET postings, excluding postings with no resolvable location.

United States: 34.8% (357)
India: 22.5% (231)
Canada: 6.9% (71)
United Kingdom: 2.5% (26)
Mexico: 2.4% (25)
Australia: 2.2% (23)

India's 22.5% share is a much larger second market than most tech roles in this series show, a reflection of how much test-automation work has moved to offshore engineering hubs and global capability centers over the last several years.

Share of SDET postings tagged with each work mode.

Onsite: 54.8% (563)
Hybrid: 25.3% (260)
Remote: 15.0% (154) (fully-remote SDET openings)

Only 15.0% of SDET postings are tagged remote, one of the more onsite-heavy roles in our data. That fits the profile: SDETs frequently test hardware, regulated systems, or on-prem infrastructure that isn't practical to validate from a home office. If you're comparing this role against its close cousin, the QA Engineer title spans a much wider range of industries and pays less on average; SDET stays closer to a pure software-engineering discipline.

The Real Employers Behind the SDET Numbers

Ranking by distinct openings rather than raw posting count, and setting aside staffing and nearshore-services firms whose "openings" are really contractor placements, a credible top-12 emerges:

Company	Distinct SDET openings
Accenture	26
Sony Interactive Entertainment	22
SpaceX (Space Exploration Technologies Corp.)	14
Apple	13
NVIDIA	11
Amazon	10
Akamai Technologies	8
Abbott Laboratories	8
Comcast	8
HighLevel	7
GE Vernova	7
DTCC	7

Gaming and hardware (Sony Interactive Entertainment, SpaceX, Apple, NVIDIA) sit alongside consulting (Accenture), cloud infrastructure (Akamai), and regulated industries (Abbott, Comcast, DTCC) rather than clustering around one sector. That spread tracks with the geography and remote data above: SDET demand is broad-based, not concentrated in any single industry vertical.

How to Use This in Your Job Search

If you're preparing an SDET job search, the data points to a specific sequence, not a longer skill list.

Confirm the floor, then move past it. Automation, a testing framework (Selenium or Playwright), Python or Java, and CI/CD are what get your resume past the first filter. If you're missing one of those four, fix that before anything else.

Pick the pipeline-ownership track over the framework-depth track. The salary data is unambiguous: a second or third testing framework barely moves your offer, but AWS, Jenkins, Docker, and CI/CD ownership add $25,000 to $41,000 over the baseline. If you have to choose where to spend your next few months of study, spend it on owning a pipeline end to end, not on learning a fourth automation library.

Treat AI-assisted testing as a baseline skill, not a bonus. It rarely appears as a named requirement in postings, but 89% of quality-engineering organizations are already running or piloting it. Being fluent with AI-assisted test generation and self-healing automation is quickly becoming assumed, the same way Git and CI/CD became assumed a decade ago.

Drill the material, then practice under real conditions. Our interview-prep courses cover the CI/CD, cloud, and automation-architecture foundations that show up in SDET interview loops. The question bank lets you drill test-design, debugging, and system-design topics one at a time. AI mock interviews let you rehearse a full round under realistic conditions, with feedback specific to test-architecture and debugging questions.

Filter the job board for your stack. Browse current SDET openings and layer on skill filters to match your target, for example SDET + AWS or SDET + Python. The board updates daily.

FAQ

Q. What is the median salary for a Software Development Engineer in Test (SDET) in 2026?

Among US postings with disclosed pay, the median base salary for a Software Development Engineer in Test (SDET) is $130,000 (n=181). Equity, bonus, and sign-on are not disclosed in postings, so total compensation at top employers runs higher than this baseline.

Q. Does knowing Selenium or Playwright pay more as an SDET?

Not by much. Selenium carries a median US base salary of $134,300 (+$4,300 over baseline, n=35) and Playwright $141,900 (+$11,900, n=26). Framework choice barely moves pay; the larger premiums sit in cloud and pipeline skills like AWS and Jenkins.

Q. What SDET skills pay the most in 2026?

AWS commands the largest premium at $171,000 median US base salary (+$41,000 over the $130,000 baseline, n=27), followed by automated testing (+$29,000), CI/CD (+$28,300), Jenkins (+$27,100), and Docker (+$25,000).

Q. Is automation a required skill for SDET jobs?

Yes, effectively. Automation appears in 87.0% of active SDET postings (894 of 1,027 analyzed), the only skill in this dataset to cross the 50% table-stakes threshold, though that figure is likely inflated slightly by industrial/controls-engineering postings that also use the word automation. Despite being nearly universal, it adds just $6,500 to the median baseline salary.

Q. How much of the SDET job market is entry-level?

Very little. Just 2.4% of SDET postings (25 of 1,027) are entry-level, while 61.8% are mid-level, 21.9% are senior, and 13.8% are staff. This is one of the more experience-gated roles in InterviewStack's job-market data.

Q. Are SDET jobs remote?

Mostly not. 54.8% of SDET postings are onsite, 25.3% are hybrid, and only 15.0% are remote, reflecting the role's ties to hardware, regulated industries, and on-prem test infrastructure at many employers.

Q. Do SDET job postings require AI or machine learning skills?

Explicitly, rarely: only about 9.6% of postings even touch the Machine Learning & AI category, and none resolve to a specific named AI skill. That undercounts real usage. The QA industry's own World Quality Report 2025-26 found 89% of quality-engineering organizations are already piloting or running AI-augmented testing workflows, meaning AI-assisted test generation is increasingly assumed even when job postings don't mention it.

The Ceiling Is in the Pipeline, Not the Test Suite

Automation got you into the room; 87% of your competition has it too. The postings and the salary data both point the same direction: the next skill worth learning as an SDET isn't a fourth testing framework, it's ownership of the cloud and pipeline infrastructure those tests run inside. That's where the $25K-41K premiums live, and it's where the market is telling you to spend your next round of study.

Why 91% of Research Scientist Jobs Now Require AI (But Not LLMs)

Gnana — Sun, 05 Jul 2026 18:28:17 +0000

For a Research Scientist, AI Isn't a Skill. It's the Subject.

Every other role in this series answers the same question: does this job require AI, yes or no. Research Scientist breaks that question. For this role, AI usually isn't a tool bolted onto the job description, it's the thing being researched. Ninety-one percent of active Research Scientist postings on the InterviewStack.io job board mention some form of AI or machine learning, drawn from 853 active listings over the trailing 90 days. Only 8.6% describe a role you could plausibly do without touching AI at all. (The "Research Scientist" title also picks up some adjacent titles, quantitative researchers, biotechnology research scientists, product researchers, so read these figures as directionally representative of AI/ML-focused research roles rather than a perfectly filtered slice.)

That number alone would make Research Scientist the most AI-saturated role tracked in this series. But it hides a fault line: companies aren't unanimous about which era of AI they want. 85.0% of postings ask for traditional machine learning and deep learning, the discipline that predates the generative AI wave by a decade, while 56.2% ask for new-wave skills like LLMs, AI agents, and retrieval-augmented generation. Those two groups overlap heavily, but they aren't the same job, and the gap between them is the real story.

Key Findings

91.4% of Research Scientist postings (780 of 853) require some form of AI or ML; only 8.6% show no AI/ML skill at all.

85.0% of postings require traditional ML/deep learning, ahead of the 56.2% that require new-wave generative AI (LLMs, agents, RAG, fine-tuning).

49.8% of postings require both traditional ML and generative AI; just 6.3% ask for generative AI with no traditional ML background.

Machine Learning is the single most-requested skill at 79.7% of postings, with AI Agents (24.5%) edging out LLMs (23.8%) among new-wave skills.

US postings requiring new-wave AI report a $206,124 median base salary (n=234) versus $179,000 without AI (n=34), a $27,124 premium.

Entry-level postings show a 39.5% new-wave AI rate, higher than junior-level (27.3%); staff-level postings have the highest rate overall at 71.4%.

Anthropic and OpenAI require new-wave AI skills in 100% of their Research Scientist postings; Google (55.1%) and Meta (59.5%) run lower, reflecting broader research portfolios beyond generative AI.

What Research Scientist Meant Before ChatGPT

Three or four years ago, a Research Scientist posting described a fairly stable job: design experiments, build and train models (often in TensorFlow or PyTorch), and publish or document results that pushed a model class forward. "AI" meant classical machine learning and deep learning research: computer vision, pre-LLM NLP, recommendation systems, reinforcement learning. Generative AI existed as a research subfield, not the industry's default framing for the whole job.

That baseline shifted fast once ChatGPT-class tools went mainstream. Survey data on researchers broadly (not limited to industry job titles) shows AI tool adoption jumping from 57% in 2024 to 84% in 2025, with usage specific to research and publication tasks growing from 45% to 62% over the same window, per Wiley's 2025 survey of 2,430 researchers. That is a one-year swing, and it lines up with what the posting data now shows: AI in some form is close to universal, but the generative-AI-specific slice is still catching up to the traditional-ML majority.

The Real Split: Traditional ML vs. Generative AI

The headline number, 91.4% "any AI," is really two overlapping populations. 85.0% of postings require traditional ML/deep learning, standard Research Scientist work for years. 56.2% require new-wave generative AI: LLMs, AI agents, RAG, prompt engineering, or a named platform like OpenAI, Anthropic, or Gemini. 49.8% ask for both, meaning half the role now expects researchers to move between classic model training and generative-AI systems work. Only 6.3% ask for generative AI with no traditional ML background at all, so the "AI-native, no ML fundamentals" researcher is still rare; the generative-AI track is mostly an extension of ML expertise, not a replacement for it.

91.4% of postings require some AI/ML skill; the traditional-ML majority (85.0%) still outpaces the new-wave generative AI slice (56.2%).

This is Layer 1: what companies explicitly write into the posting, who's hired to build, train, fine-tune, or evaluate AI systems as the deliverable, not who merely uses an AI tool to work faster. The title sample makes the split visible: postings like "Elite Research Scientist, Frontier AI Evaluation" and "Researcher, Misalignment Research" sit alongside "ML Research Scientist, Co-Folding and Affinity" and "Research Scientist, Catalyst Simulation," where deep learning is the method but the subject is chemistry or biology, not AI itself.

The Skills That Separate Classic Research From Frontier Research

Machine Learning is the closest thing this role has to table stakes: it appears in 79.7% of postings, nearly 1.7x the rate of the next most-common individual skill. Deep Learning/Neural Nets follows at 47.9%, still solidly traditional. Below that, the new-wave cluster has an order worth noticing: Generative AI leads at 29.1%, but AI Agents (24.5%) edges out LLMs (23.8%) for second place. Companies aren't just asking for researchers who understand language models, they're asking for researchers who can build systems that act, chain reasoning steps, and operate with some autonomy. Everything past that (MLOps at 4.5%, RAG at 3.8%, LLM Fine-Tuning at 2.2%, named platforms like Anthropic/Claude and Hugging Face at 2.1% each) is a differentiator, not a baseline.

AI Agents (24.5%) now outranks LLMs (23.8%) as an individually named skill, a signal that companies are hiring for agentic system-building, not just LLM familiarity.

Skill	% of postings	What it signals
Machine Learning	79.7%	Baseline expectation across the role
Deep Learning / Neural Nets	47.9%	Standard for anyone touching modern architectures
Generative AI	29.1%	The umbrella term for LLM-era work
AI Agents	24.5%	Autonomous, multi-step systems, not single-turn chat
LLMs	23.8%	Direct language-model research or application
MLOps	4.5%	Production deployment, not just research
RAG	3.8%	Retrieval-augmented systems specifically
LLM Fine-Tuning	2.2%	Model customization as a named deliverable

Do You Get Paid More for Building the Frontier?

Among US postings with disclosed salary (base salary only; equity, bonus, and other compensation aren't captured in job listings), Research Scientist roles that require new-wave generative AI skills report a median of $206,124 (n=234). Roles with no AI requirement at all report a median of $179,000, though that figure rests on a thin sample (n=34) and should be read as directional rather than definitive. That's a $27,124 gap, roughly 15% above the no-AI baseline, for postings that specifically ask for LLM, agent, or generative-AI experience over classic ML alone.

Postings requiring new-wave AI skills report a $27,124 higher US base salary median than postings with no AI requirement at all.

That premium is smaller than some other roles in this series show, which tracks with the rest of the data: Research Scientist salaries are already high across the board, because the baseline job (any ML/DL research) commands a premium versus most engineering roles. The generative-AI skew adds meaningfully on top of that, but it's closer to the gap between a strong ML researcher and one who can also ship agentic or LLM-based systems than to the gap between entry-level and senior pay.

Even the Traditional-ML Half Uses AI Every Day

It would be easy to read the 91.4% "any AI" figure and the 56.2% new-wave figure and conclude that the remaining postings, especially the 8.6% with no AI mention at all, describe scientists who don't touch AI in daily work. That conclusion doesn't hold up.

The explicit posting numbers only capture Layer 1: what a company writes down as a hiring requirement, researchers hired specifically to build, fine-tune, or evaluate AI systems. They miss Layer 2, the ambient layer: the AI tools a researcher reaches for regardless of whether the posting ever says "AI." Wiley's 2025 survey found 85% of researchers report AI improved their efficiency, and they overwhelmingly reach for general-purpose tools over specialized ones: 80% use mainstream tools like ChatGPT, versus just 25% using dedicated AI research assistants. More than half now report using AI for peer review, a workflow that has nothing to do with whether their job title mentions generative AI, and a nationally representative survey cited in Nature put general research-tool use at 65% of academic scientists. None of that shows up in a posting that lists "TensorFlow" and "experimental design."

So the honest reading is this: 56.2% of postings require you to build generative AI systems as the deliverable. Effectively all Research Scientists, including the ones in postings that only mention classic ML/DL, are now expected to use AI tools like ChatGPT or Copilot-class assistants to write analysis code, synthesize literature, and speed up experimentation. What varies isn't whether AI matters, it's whether AI is the subject of the research or the tool used to do it faster. Adoption is also outpacing trust: concerns about AI inaccuracy and hallucination among researchers rose from 51% to 64% year over year alongside the adoption jump, a useful check against unqualified enthusiasm.

Who's Leading the Shift?

Seniority tells a more layered story than "senior roles lead." Senior postings carry the largest share of the role by volume (68.0% of all 853 postings) and a solid 58.3% new-wave AI rate. Staff-level postings are rare (3.3% of the total) but carry the highest AI rate of any tier at 71.4%, a sign that the most senior research roles skew hardest toward generative AI. The more interesting wrinkle sits at the bottom: entry-level postings show a 39.5% AI rate, notably higher than the 27.3% rate for junior-level postings, likely because PhD-track entry hires ("Research Scientist Intern" and similar) get staffed directly onto frontier AI projects, while the junior tier, still building foundational research experience, skews more traditional.

Staff-level postings lead at 71.4% AI adoption; entry-level (39.5%) surprisingly outpaces junior-level (27.3%).

Industry data only clears a credible sample size in two categories, both broadly "tech": technology (29.7% of postings, 68.0% AI rate) and software (15.9% of postings, 57.0% AI rate). Both run well above the role-wide 56.2% baseline, which is expected given how much of this role's demand sits inside AI-native companies to begin with.

Technology (68.0%) and software (57.0%) both post AI rates above the 56.2% role-wide baseline.

The company data draws the same distinction sharper. Google (49 postings) and Meta (42 postings) hire the most Research Scientists overall, but their AI rates, 55.1% and 59.5%, sit close to the role-wide average, because both run large research organizations that span far beyond generative AI. Frontier AI labs look different: Anthropic (13 postings) and OpenAI (10 postings) each require new-wave AI skills in 100% of their listed roles. At a lab built entirely around generative AI, there is no "traditional-only" research track left to hire for.

Company	Postings	New-wave AI rate
Google	49	55.1%
Meta	42	59.5%
NVIDIA	30	80.0%
Adobe	23	69.6%
Microsoft	14	85.7%
Anthropic	13	100%
Huawei Technologies Canada	11	90.9%
OpenAI	10	100%
ifm	10	100%
SandboxAQ	10	50.0%

Adobe's two dataset entries ("Adobe" and "Adobe Inc.") are combined here (14+9 postings, 9+7 AI-requiring). A staffing/recruiting marketplace that appeared in the raw data at the same posting volume as Adobe and Microsoft was excluded from this table; its listings are contractor postings on behalf of AI labs, not its own headcount.

How to Use This in Your Job Search

If you're aiming at frontier AI labs (the Anthropic/OpenAI end of this data), assume every posting is a generative-AI posting: prepare for LLM, agent, and evaluation-focused technical rounds, not general ML theory. If you're aiming at Google, Meta, or a research org with a broader mandate, expect a mix, and read the posting closely rather than assuming either. Either way, treat AI-assisted research tools (literature synthesis, code generation, experiment analysis) as baseline fluency rather than a differentiator; the survey data above suggests most of your competition already uses them.

To close the gap between "familiar with ML" and "can build the systems these postings describe," practice technical and research-methodology interviews with AI mock interviews that simulate the evaluation-and-reasoning questions frontier labs ask. Use the question bank to drill AI agents, RAG, and model evaluation, and lean on interactive courses if you need foundational ML or deep learning depth before layering generative AI skills on top. When you're ready, browse current Research Scientist openings, or narrow to a differentiator skill with a filtered search for AI Agents or Generative AI roles.

FAQ

Q. How many Research Scientist jobs require AI skills in 2026?

Among the 853 active Research Scientist postings analyzed on the InterviewStack.io job board, 91.4% require some form of AI or machine learning (780 postings). 85.0% require traditional ML or deep learning, and 56.2% require new-wave generative AI skills like LLMs, AI agents, or RAG.

Q. Is Research Scientist mostly a generative AI role now?

Not entirely. Traditional machine learning and deep learning still appear in more postings (85.0%) than new-wave generative AI (56.2%). Roughly half of postings (49.8%) require both, and only 6.3% ask for generative AI skills without any traditional ML background, meaning classic ML expertise remains the more common baseline.

Q. Which AI skill shows up most in Research Scientist postings?

Machine Learning tops the list at 79.7% of postings, followed by Deep Learning/Neural Nets at 47.9%. Among new-wave skills, Generative AI (29.1%) and AI Agents (24.5%) lead, with AI Agents edging out LLMs (23.8%) as the more commonly named skill.

Q. Do Research Scientists earn more with generative AI skills?

Among US postings with disclosed salary, Research Scientist roles requiring new-wave generative AI skills report a median base salary of $206,124 (n=234), compared to $179,000 (n=34) for roles without any AI requirement, a $27,124 premium. The no-AI figure rests on a thin sample (n=34) and should be read as directional rather than definitive. This is US base salary only; equity and bonus are not included.

Q. Do entry-level Research Scientist jobs require AI?

Yes, and more than you'd expect: 39.5% of entry-level postings require new-wave AI skills, actually higher than the 27.3% rate for junior-level postings. Senior roles carry the largest AI-skill volume overall (68.0% of all postings), and staff-level roles have the highest AI rate of any tier at 71.4%.

Q. Which companies are hiring the most AI-focused Research Scientists?

Google (49 postings) and Meta (42 postings) post the most Research Scientist openings overall, but frontier AI labs show the sharpest AI concentration: Anthropic and OpenAI each require new-wave AI skills in 100% of their Research Scientist postings, compared to 55.1% at Google and 59.5% at Meta.

Q. If a Research Scientist posting doesn't mention AI, does that mean the role doesn't use it?

No. Only 8.6% of postings mention no AI or ML skill at all, and even that figure likely overstates how many Research Scientists work without AI day to day. Survey data on researchers broadly shows AI tool adoption jumped from 57% to 84% between 2024 and 2025 (Wiley 2025), with most of that usage running through general-purpose tools like ChatGPT rather than specialized research-AI products that job postings rarely bother to state.

Pick Your Half of the Stack

Research Scientist isn't splitting into "AI roles" and "non-AI roles," it's splitting into classic ML/DL research and generative-AI systems research, with half the market now asking for both. The traditional track isn't going away (it's still the more common requirement), but the generative-AI track carries the salary premium, the staff-level concentration, and the frontier-lab hiring. Figure out which half of the stack you're building toward, and prepare for that one specifically rather than treating "AI skills" as a single box to check.

Data Engineer vs Business Intelligence Analyst 2026: $25,700 Apart

Gnana — Wed, 01 Jul 2026 01:08:34 +0000

Both Roles Run on SQL. The Pay Gap Lives in the Infrastructure.

SQL is not a Data Engineer skill or a Business Intelligence Analyst skill. It belongs to both, at the same frequency: SQL appears in 67.5% of Data Engineer postings and 67.5% of Business Intelligence Analyst postings across the InterviewStack.io job board as of June 2026, covering 8,774 active Data Engineer listings and 2,440 Business Intelligence Analyst listings. The rates are tied to the decimal.

The $25,700 salary gap between these roles has nothing to do with SQL fluency. It comes from the layer that surrounds SQL in each job. Data Engineers use SQL inside pipelines: they write transformations that run on Spark, orchestrated by Airflow, deployed via CI/CD, monitored with observability tooling, and stored in cloud warehouses. Business Intelligence Analysts use SQL to pull data into dashboards, where Power BI (60% of postings), Tableau (37%), and Excel (31%) are the destinations. Both populations need SQL. Only one needs the infrastructure that feeds it.

Dataset note: the Data Engineer sample is tightly scoped to software and data-platform engineering roles. The Business Intelligence Analyst sample draws from a broader analytics analyst population; a random title review found roughly 15% of entries were adjacent roles (government intelligence analysts, compensation analysts, and market intelligence specialists whose descriptions include "business intelligence"). The core BI tool signals (Power BI at 60%, Tableau at 37%) and the salary median reflect genuine demand from BI-oriented roles and are directionally reliable at this contamination level.

Key Findings

SQL is demanded at 67.5% for both Data Engineer and Business Intelligence Analyst roles; the two roles share this foundation at identical depth across 11,214 active postings.

Data Engineers earn a median US base salary of $155,500 (n=1,598) vs. $129,800 for Business Intelligence Analysts (n=445), a $25,700 (19.8%) gap.

Data Engineer postings outnumber Business Intelligence Analyst postings 3.6 to 1: 8,774 vs. 2,440 active listings.

The Jaccard overlap between the two skill sets is 46%, meaning less than half the skills transfer directly.

Business Intelligence Analyst has a higher entry-level share: 5.9% of postings vs. 2.6% for Data Engineer.

Data Engineers have more remote flexibility: 21% of DE postings are tagged remote vs. 15% for Business Intelligence Analysts.

The highest-paying BI Analyst skills (BigQuery at $152,500, dbt at $149,500, Looker at $149,500) are all infrastructure-adjacent. Leaning toward the DE side of the stack is how BI Analysts close the pay gap.

The Short Answer

Data Engineers earn more, have roughly 3.6 times as many open positions, and require a deeper infrastructure skill set. Business Intelligence Analysts have a lower barrier to entry and a skill profile built for stakeholder-facing reporting rather than pipeline construction. Both roles need SQL at the same intensity; neither is more SQL-heavy than the other.

	Data Engineer	Business Intelligence Analyst
Median US base salary	$155,500	$129,800
Active postings	8,774	2,440
Top skill	Data Pipelines (72%)	Data Visualization (68%)
Remote share	21%	15%
Entry-level share	2.6%	5.9%
Skill overlap (Jaccard)	46% shared	N/A

What Does Each Role Actually Do?

A Data Engineer's job is to make data reliable, scalable, and accessible. Their week looks like building and maintaining ETL/ELT pipelines, writing Python and SQL transformations, debugging pipeline failures, and incrementally improving infrastructure: adding monitoring to an Airflow DAG (Apache Airflow is the open-source scheduler most data teams use to orchestrate pipelines), optimizing a Spark job, migrating a warehouse to Snowflake. The output is infrastructure that analysts and data scientists depend on to do their work. The exclusive skills (Spark, CI/CD, Kafka, Airflow) signal that production reliability and scale are core concerns, not optional. Machine learning appears in 19% of Data Engineer postings, specifically in roles hired to build the pipelines that feed AI systems.

A Business Intelligence Analyst's job is to turn that infrastructure's output into decisions. Their week looks like building and maintaining dashboards in Power BI or Tableau, writing SQL queries to answer business questions, and presenting findings to stakeholders who are not technical. The exclusive skills (Tableau, Excel, Storytelling, Forecasting) signal that translating data into insight for non-technical audiences is the primary output. "Storytelling" appears in 9% of BI Analyst postings and has no counterpart in Data Engineer hiring. Machine learning shows up in 10% of BI Analyst postings, typically in roles expected to incorporate predictive outputs into dashboards rather than build the models themselves.

Both roles now work alongside ambient AI tools regardless of what the job posting says. Microsoft Copilot is embedded directly in Power BI (present in 60% of BI Analyst postings), bringing AI-native anomaly detection and natural-language querying into the tool most BI Analysts use every day. For Data Engineers, AI-assisted coding tools have become standard across data and analytics engineering work. GitHub Copilot and ChatGPT are now routinely used for authoring SQL transformations and Python pipeline functions. The job posting numbers measure who is hired to build AI systems; the survey numbers measure who uses AI tools every week as a baseline expectation, which is nearly everyone in either role.

What Skills Do Both Roles Require?

Both roles demand SQL, Python, and data quality practices in the majority of postings. The shared foundation is more extensive than most people assume.

Share of postings that mention each skill for Data Engineer and Business Intelligence Analyst, drawn from the union of both roles' top 30 skill lists.

The SQL parity is the headline: despite feeling like very different roles, both are built on structured query work at comparable intensity. Beyond SQL, the shared profile includes:

Python: 67% of Data Engineer postings vs. 40% for Business Intelligence Analyst. Shared, but with a 27-point gap. Python in DE postings signals pipeline code; in BI Analyst postings it signals analytical scripting and automation.
Data Pipelines: 72% DE vs. 30% BI. The BI Analyst fraction represents roles that interact with the pipeline layer, not build it.
Data Visualization: 27% DE vs. 68% BI. Conceptually shared, but at opposite intensities.
Data Quality: 43% DE vs. 20% BI. Both roles care about data accuracy; Data Engineers are also responsible for enforcing it structurally.
Snowflake, Databricks, Azure, AWS: present in both roles, but at much higher rates in Data Engineer postings.

The 46% Jaccard overlap means roughly half the skill set transfers across the two roles. A BI Analyst who knows SQL, Python, Snowflake, and data quality practices already holds a meaningful chunk of what Data Engineer postings require. The gap is in what they have never had to build: pipelines, orchestration, and cloud infrastructure.

Where Do the Roles Diverge?

Exclusive to Data Engineer (skills present in DE postings at meaningful rates but not in BI Analyst postings at comparable levels):

Apache Spark: 33% of Data Engineer postings. Spark signals distributed compute: the ability to process datasets too large for a single machine or a standard warehouse query.
CI/CD: 30%. Data Engineers are expected to ship pipeline code through versioned, tested release pipelines. This is software engineering practice applied to data work.
Data Architecture: 26%. Designing the overall structure of the data platform, not just querying against it.
Airflow: 25%. The dominant orchestration tool for scheduling and monitoring data workflows.
Google Cloud: 24%. Triple-cloud exposure (AWS 43%, Azure 37%, Google Cloud 24%) is common in DE postings.
Kafka: 19%. Real-time event streaming, the signal for roles that process data as it arrives rather than in batches.

Exclusive to Business Intelligence Analyst (skills present in BI Analyst postings but not in Data Engineer postings at comparable rates):

Tableau: 37%. The dominant visualization tool in non-Microsoft environments.
Excel: 31%. Ubiquitous in BI Analyst postings for reporting, modeling, and stakeholder delivery.
Statistics: 18%. BI Analysts are expected to apply statistical reasoning to business problems; Data Engineers are not.
Looker: 11%. The modern data-app layer for embedded analytics.
Forecasting: 9%. Projecting future business metrics from historical data.
Storytelling and Stakeholder Management: each around 9%. Both signal that the job includes presenting to non-technical decision-makers. Data Engineer postings carry neither.

The dividing line is clear: infrastructure vs. presentation. Data Engineers build reliable data systems; Business Intelligence Analysts translate those systems' output into business decisions.

Which Pays More?

Data Engineers earn more. Among US postings with consistent salary disclosure (where wage-transparency laws produce comparable figures), the median base salary is $155,500 for Data Engineers (n=1,598) vs. $129,800 for Business Intelligence Analysts (n=445). These are base salary figures only; equity, RSUs, bonuses, and sign-on are not disclosed in postings and are not captured here. Total compensation at top tech and finance employers runs meaningfully higher on both sides.

Median US base salary for Data Engineer and Business Intelligence Analyst postings, overall and for selected shared skills. Base salary only, US postings only.

The highest-paying skills in BI Analyst postings reveal where the pay ceiling lives: BigQuery ($152,500, n=25), dbt (a SQL transformation framework that runs inside the data warehouse, $149,500, n=61), Looker ($149,500, n=55), and Snowflake ($147,800, n=79) each carry premiums of $18,000 to $23,000 above the $129,800 BI Analyst baseline. Every one of them is an infrastructure-adjacent tool, closer to the Data Engineer side of the stack than the Excel-and-dashboard side. A BI Analyst who adds genuine cloud warehouse or data transformation skills can close a substantial portion of the pay gap without switching roles.

On the Data Engineer side, premium skills push well above the $155,500 median: Distributed Systems ($180,000, n=147), Observability ($172,800, n=319), dbt ($164,500, n=340), Kafka ($163,000, n=337), Apache Spark ($160,000, n=526), and Airflow ($160,000, n=417). Infrastructure depth pays more than presentation breadth across both sides of this comparison. See the full Data Engineer skills breakdown for the complete salary-by-skill picture on the DE side.

Which Has More Openings?

Data Engineer postings outnumber Business Intelligence Analyst postings 3.6 to 1: 8,774 vs. 2,440 active listings. That volume gap matters for job search strategy. There are roughly 3.6 times as many positions to apply for in the DE market.

The seniority picture differs in two important ways. Business Intelligence Analyst has a higher entry-level share: 5.9% of BI Analyst postings are explicitly entry-level vs. 2.6% for Data Engineer. Both roles are dominated by mid-level work (64% for BI Analyst, 54% for Data Engineer), but the BI path is roughly twice as accessible at the starting level. At the upper end, Data Engineering skews harder toward staff and above (14% staff/lead/principal vs. 9% for BI Analyst), which means the long-term IC ceiling is higher on the DE track.

For remote work, Data Engineers have a modest advantage: 21% of DE postings are tagged remote vs. 15% for Business Intelligence Analysts. Both roles are predominantly onsite (50% for Data Engineer, 58% for Business Intelligence Analyst). Neither stands out for flexibility compared to software engineering or ML roles.

Geography: the US leads for both, at 32% of Data Engineer postings and 34% of Business Intelligence Analyst postings. India is a significant Data Engineer market (17%), reflecting global capability centers that build data infrastructure for US and UK clients. Business Intelligence Analyst postings have a notable Brazil presence (7.4%), reflecting strong regional demand for analytics reporting roles. Browse Data Engineer openings or Business Intelligence Analyst openings filtered to your location to see the regional picture directly.

Which Should You Choose?

Choose Data Engineer if you:

Want to build the systems that produce data, not analyze what those systems produce
Have or want to develop programming depth: Python for pipeline code, not just analysis scripts
Are drawn to infrastructure concerns: reliability, scale, orchestration, observability
Can accept a steeper entry bar (2.6% of postings are entry-level) in exchange for a 3.6x larger job market and a $25,700 salary premium
Want the higher career ceiling: 14% of DE postings are staff/lead/principal level vs. 9% for BI Analyst

Choose Business Intelligence Analyst if you:

Are stronger in business communication and data presentation than in software engineering
Want more entry-level access (5.9% of postings) while building toward mid and senior roles
Are comfortable in a dashboard-building workflow centered on Power BI, Tableau, or Looker
Have or are building statistical reasoning skills (forecasting, trend analysis) that DE roles rarely require
Prefer stakeholder-facing work: the "storytelling" and "stakeholder management" signals in BI Analyst postings have no counterpart in Data Engineer hiring

A BI Analyst crossing into Data Engineering has a meaningful head start: the shared SQL foundation plus Python and data quality skills covers a large portion of what DE postings require. The remaining gap is in infrastructure tools (Spark, Airflow, Kafka, CI/CD) and cloud depth. Practice data pipeline design and system design scenarios before your first DE interview to close that gap.

How to Use This in Your Job Search

For both roles, start with the shared foundation. SQL at production depth (window functions, CTEs, query tuning) is required for either path, and drilling it in realistic interview conditions matters more than reading documentation. The InterviewStack.io question bank covers SQL, data modeling, and Python topics that appear in both types of interviews.

From there the paths diverge. Data Engineer candidates should add one cloud platform and one orchestration tool to their story before applying: browse Data Engineer postings filtered to AWS to see which cloud dominates the segment you are targeting. Business Intelligence Analyst candidates should ensure their dashboard portfolio includes at least one of the dominant tools: Power BI, Tableau, or Looker. Browse Business Intelligence Analyst openings to see what the current market expects in your region.

For interview preparation, AI mock interviews let you practice role-specific scenarios: pipeline design and troubleshooting for DE candidates, dashboard requirements and business framing for BI Analyst candidates. Interactive courses cover SQL, Python, and statistics foundations applicable to both paths.

FAQ

Q. What is the salary difference between Data Engineer and Business Intelligence Analyst in 2026?

Data Engineers earn a median US base salary of $155,500 vs. $129,800 for Business Intelligence Analysts, a $25,700 (19.8%) gap. Both figures are base salary only from postings with US wage disclosure; equity and bonuses are not reflected.

Q. Do both Data Engineer and Business Intelligence Analyst roles require SQL?

Yes, and at almost exactly the same rate. SQL appears in 67.5% of Data Engineer postings and 67.5% of Business Intelligence Analyst postings across 11,214 active listings. The two roles share a SQL foundation; the divergence is in everything built on top of it.

Q. Which role is easier to break into as a beginner?

Business Intelligence Analyst has a higher entry-level share: 5.9% of BI Analyst postings are explicitly entry-level vs. 2.6% for Data Engineer. Both are predominantly mid-level roles, but BI Analyst is roughly twice as accessible at the entry tier.

Q. What skills separate Data Engineers from Business Intelligence Analysts?

Data Engineers own the infrastructure layer: Apache Spark (33%), CI/CD (30%), Airflow (25%), Kafka (19%), and cloud architecture. Business Intelligence Analysts own the presentation layer: Tableau (37%), Excel (31%), Storytelling (9%), and Forecasting (9%). The overlap is 46% by Jaccard similarity.

Q. Which role has more job openings in 2026?

Data Engineer postings outnumber Business Intelligence Analyst postings 3.6 to 1: 8,774 active Data Engineer listings vs. 2,440 Business Intelligence Analyst listings. The US is the top market for both, at 32% of Data Engineer postings and 34% of Business Intelligence Analyst postings.

Make the Call

The data frames a concrete choice: more entry points and presentation-layer work on the BI Analyst side; higher pay, more volume, and infrastructure work on the Data Engineer side. SQL ties the two roles together at the foundation. What you build on top of it is the actual decision. Browse live Data Engineer postings or Business Intelligence Analyst postings on InterviewStack.io to see what the current market is asking for.

Systems Engineer vs Cloud Architect 2026: More Jobs, Less Pay

Gnana — Tue, 30 Jun 2026 02:07:14 +0000

More Jobs, Lower Pay, Less Flexibility

Conventional logic says the larger job market is the safer bet. Systems Engineering defies that. There are nearly 5x as many active Systems Engineer postings as Cloud Architect postings (8,990 versus 1,898 on the InterviewStack.io job board as of June 2026), yet the median US base salary runs $25,000 lower ($140,000 versus $165,000). The flexibility picture inverts too: 68% of Systems Engineer roles require full onsite presence, compared to 44% for Cloud Architect.

The paradox dissolves once you look at who is actually hiring. Systems Engineer titles are dominated by Northrop Grumman, General Dynamics, Leidos, Anduril, and Boeing, defense and aerospace contractors where onsite presence is a given and salary bands reflect government contractor structures. Cloud Architect postings cluster around Kyndryl, NVIDIA, Red Hat, and Accenture, tech and consulting firms that compete for cloud talent globally and price accordingly. The roles share a 33% Jaccard skill overlap, enough common ground to move between them deliberately but enough divergence to make the paths feel genuinely different.

Key Findings

8,990 active Systems Engineer postings versus 1,898 Cloud Architect postings, a 4.7x volume gap.

Median US base salary: $140,000 for Systems Engineers (n=3,580) versus $165,000 for Cloud Architects (n=399), a $25,000 (18%) gap.

Skill overlap: 0.33 Jaccard similarity across top-30 skill sets; both roles share automation, Python, monitoring, and AWS/Azure.

Work mode: Systems Engineers work onsite 68% of the time; Cloud Architects are onsite 44% and hybrid 43%.

Entry-level share: 3.1% for Systems Engineer versus 0.9% for Cloud Architect; neither role has a genuine junior track.

AI-adjacent skills command significant salary premiums in both roles: LLM skills reach $184,500 for Systems Engineers; Generative AI reaches $186,200 for Cloud Architects.

Both roles are senior-skewed: combined senior and staff postings make up 37% of Systems Engineer and 32% of Cloud Architect openings.

	Systems Engineer	Cloud Architect
Active postings	8,990	1,898
Median US base salary	$140,000	$165,000
Salary premium	(baseline)	+$25,000 (18%)
Remote share	9%	19%
Hybrid share	27%	43%
Entry-level share	3.1%	0.9%
Top skill	Automation (26%)	Azure (44%)
Skill overlap (Jaccard)	33% shared	N/A

Dataset note: Both classifiers capture adjacent roles: the Systems Engineer dataset includes security technicians, IT infrastructure engineers, and platform engineers (roughly one posting in five); the Cloud Architect dataset captures mechanical, facility, and hardware systems architects pulled in by the "Architect" keyword (roughly one in four). Skills and salary figures reflect the full blended pool and should be read as directionally accurate for the core role.

What Do Systems Engineers and Cloud Architects Actually Do?

Systems Engineers own the full lifecycle of complex systems: writing requirements, integrating hardware and software components, validating performance, and managing configuration through deployment. In practice this means living in requirements documents, integration test plans, and coordination meetings between hardware, software, and program management. The exclusive skills confirm the context: System Integration (13%), Windows (11%), VMware (8%), and MATLAB in the broader skill list all signal on-premises, embedded, or defense-platform work. These are not cloud-native roles.

Cloud Architects design and own cloud infrastructure at scale. A typical week involves translating product or business requirements into infrastructure designs, writing Terraform or CloudFormation to provision environments, tuning Kubernetes clusters, and advising stakeholders on cost and reliability trade-offs. The exclusive skills say it plainly: Cloud Architecture (22%), Scalability (22%), Infrastructure as Code (19%), Docker (13%), Observability (13%), IAM (11%), and Microservices (12%). This is a role defined by distributed systems design and cloud-native toolchain ownership.

What Skills Do Both Roles Share?

Both roles require automation, Python, monitoring, agile, and Linux as a working foundation. AWS and Azure appear in both, though at very different rates: AWS is in 12% of Systems Engineer postings versus 40% of Cloud Architect postings; Azure is in 11% versus 44%. Python is the closest thing to a true bridge, appearing in 23% of Systems Engineer postings and 22% of Cloud Architect postings, though what engineers do with it differs: automation scripting in SE, IaC tooling and pipeline work in CA.

Top shared and role-exclusive skills for Systems Engineer (emerald) and Cloud Architect (sky). Frequency reflects share of active postings for each role on the InterviewStack.io job board, June 2026.

Browse Systems Engineer postings requiring Python or Cloud Architect postings requiring Terraform to see how the same skill surfaces in different work contexts.

Where Do the Roles Diverge?

Systems Engineer's exclusive skills cluster around on-premises and integration work: System Integration (13%), Windows (11%), TypeScript (11%), Technical Documentation (10%), Project Management (10%), and VMware (8%). The TypeScript signal is worth noting, though with a caveat: it likely reflects a mix of software-intensive SE work (platform engineers and DevOps-adjacent SEs) and software developers who use "Systems Engineer" in their titles, a known overlap in this dataset. This is not a single monolithic role; it spans from embedded firmware to enterprise IT systems to defense electronics.

Cloud Architect's exclusive skills reflect cloud-native design at every layer: Google Cloud (24%), Cloud Architecture (22%), Scalability (22%), Infrastructure as Code (19%), Docker (13%), Observability (13%), APIs (12%), Microservices (12%), Java (11%), and IAM (11%). The breadth here is deliberate. Cloud Architects are expected to own the entire infrastructure surface: compute, containers, networking, identity, observability, and API design are all in scope.

A 33% Jaccard overlap means you have a transferable foundation if you hold AWS or Azure, Python, and Kubernetes experience. Moving from SE to CA requires adding IaC fluency, scalability-first design thinking, and cloud-native toolchain depth. Moving the other direction means building familiarity with on-premises integration, systems requirements processes, and hardware-adjacent constraints that cloud-native engineers rarely encounter.

Which Role Pays More, and Why?

All salary figures below are US base salary only from postings with disclosed compensation. Equity, bonus, and sign-on are not included in posting data, so total compensation at top employers runs higher than these numbers reflect.

Cloud Architect has the higher baseline at $165,000 versus $140,000 for Systems Engineer. The counterintuitive finding: the commodity cloud skills that define Cloud Architect work sit at or below the role median. Azure postings at $165,000 are exactly at baseline. Terraform ($162,500) and Kubernetes ($165,200) are at or near the baseline. The real premiums in CA come from engineering depth: Generative AI ($186,200, n=26), Observability ($177,500), Python ($177,000), Scalability ($175,000), and Cloud Security ($174,100).

Median US base salary for selected skills. Base salary only; equity and bonus excluded. Cloud Architect baseline: $165,000 (n=399). Systems Engineer baseline: $140,000 (n=3,580).

For Systems Engineers, Kubernetes ($162,000, +$22K) and CI/CD ($160,000, +$20K) are the clearest premium signals in the common skill tier. AI-adjacent skills reach further: LLM-related skills range from $165,500 to $184,500 depending on the specific variant, and Generative AI with $167,000 above the $140K baseline. These premiums reflect a distinct sub-segment of Systems Engineers building AI-enabled platforms, not the traditional defense-contractor cohort.

Job postings rarely list AI tools as explicit requirements; Stack Overflow's 2025 Developer Survey found 84% of developers use AI tools regularly, but employers treat that as ambient. The salary data is more honest: in both roles, AI expertise commands a meaningful premium. For Cloud Architects specifically, the stakes are structural: Datadog's State of AI report found 69% of organizations run three or more AI models in production, and Cloud Architects are the engineers designing and operating that infrastructure.

How Accessible Is Each Role, and Where Are the Jobs?

Neither role has a genuine junior track. Systems Engineer has 3.1% of postings at entry level; Cloud Architect has just 0.9%. The practical signal: Cloud Architect titles go to engineers who already have 3-5 years of cloud infrastructure experience, typically from a DevOps, cloud engineer, or platform engineering background.

Volume shapes accessibility differently. Systems Engineer's 4.7x advantage is real but geographically concentrated: 62% of postings are in the United States, weighted toward defense-industrial hubs like Northern Virginia, Huntsville, and San Diego. Cloud Architect has a more global footprint: 37% US and 12% India, reflecting the distributed hiring model of global consulting and cloud-services firms. If you are outside the US, Cloud Architect has a proportionally larger market.

The work-mode gap is the sharpest practical difference. Systems Engineer is 9% remote and 68% onsite, consistent with defense-sector and hardware-lab requirements. Cloud Architect is 19% remote and 43% hybrid: roughly 1 in 5 roles is fully remote, and more than 2 in 5 offer hybrid arrangements. If workplace flexibility matters to your decision, browse remote Cloud Architect roles to gauge real-time demand in your region.

Which Role Should You Choose?

Choose Systems Engineer if you:

Want to work with physical, embedded, or defense-platform systems rather than cloud infrastructure
Are comfortable with (or open to) onsite work, including roles with security clearance requirements
Need a larger raw job market: 8,990 active openings means more hiring cycles across more employers, including a small but real entry-level tier
Come from an electrical, mechanical, or systems-software background and want to stay in that problem space

Choose Cloud Architect if you:

Already have 3-5 years of cloud infrastructure or platform engineering experience and want to step into design ownership
Prioritize workplace flexibility: Cloud Architect offers more than twice the remote-work share of Systems Engineer, and 43% hybrid
Want the higher salary floor: the $165K median is the starting point, with observability, Python, scalability, and AI infrastructure skills adding $10-22K above that
Are building toward a role at the center of AI production infrastructure; Cloud Architects are increasingly responsible for the platforms that run AI in production

How Should You Use This in Your Job Search?

Start with the live market on InterviewStack.io for Cloud Architect openings or Systems Engineer openings filtered to your target region and work mode. For interview preparation, AI mock interviews cover the system design and infrastructure architecture questions common to both pipelines. Drill cloud infrastructure, distributed systems, and systems design concepts through the question bank; the foundational topics overlap between both roles even when the specific toolchains do not.

FAQ

Q. What is the salary difference between Systems Engineers and Cloud Architects in 2026?

Cloud Architects earn a median $165,000 US base salary versus $140,000 for Systems Engineers, a $25,000 (18%) gap. All figures are US base salary only from postings with disclosed compensation; equity and bonus are not included. The gap is partly explained by employer type: Systems Engineers are concentrated in defense and aerospace (lower-paying, onsite employers), while Cloud Architects cluster in tech, consulting, and cloud-native firms.

Q. Which role is easier to break into as an entry-level candidate?

Systems Engineer is more accessible: 3.1% of its postings are entry-level versus just 0.9% for Cloud Architect. The absolute volume also helps: 8,990 Systems Engineer postings versus 1,898 Cloud Architect postings means more raw openings at every level. Cloud Architect is effectively a senior hire; the title implies architecture ownership that most employers treat as a mid-level-minimum bar.

Q. What skills do Systems Engineers and Cloud Architects share?

Both roles share automation, Python, monitoring, agile, and Linux as a common foundation, though frequencies differ sharply. Azure appears in 44% of Cloud Architect postings but only 11% of Systems Engineer postings. The Jaccard similarity across both roles' top-30 skill sets is 0.33, meaning they share about one-third of their skill profiles despite overlapping titles.

Q. Which role offers more remote work flexibility?

Cloud Architect is substantially more flexible: 19% of postings are fully remote and 43% are hybrid, versus Systems Engineer's 9% remote and 27% hybrid. The driver is employer type: Systems Engineer hiring is dominated by defense contractors and aerospace firms that require onsite presence, often with security clearances. Cloud Architect employers skew toward tech companies and consulting firms with distributed teams.

Q. What skills are exclusive to Cloud Architect versus Systems Engineer?

Cloud Architects require skills that rarely appear in Systems Engineer postings: Google Cloud (24%), Cloud Architecture (22%), Scalability (22%), Infrastructure as Code (19%), Docker (13%), Observability (13%), IAM (11%), and Microservices (12%). Systems Engineers have skills exclusive to on-premises and hardware-adjacent contexts: System Integration (13%), Windows (11%), TypeScript (11%), VMware (8%), and Technical Documentation (10%).

Q. Which skills pay the highest premium for Systems Engineers and Cloud Architects?

For Systems Engineers (baseline $140,000 US median), Kubernetes ($162,000) and CI/CD ($160,000) add the strongest premiums among common skills. AI-adjacent skills reach higher: LLM-related skills range from $165,500 to $184,500 depending on the specific variant, and Generative AI reaches $167,000. For Cloud Architects (baseline $165,000), Generative AI ($186,200), Observability ($177,500), and Python ($177,000) sit above the baseline; core cloud tools like Azure and Terraform sit at or below the role median.

Where Do Both Careers Lead?

Both roles are healthy and actively hiring, just in very different ecosystems. Systems Engineering offers scale, variety, and a lower barrier to entry, but the market is defense-heavy and largely onsite. Cloud Architect offers a higher pay floor, genuine flexibility, and a front-row seat to AI infrastructure buildout, but it is a senior-minimum track with a much smaller job pool. The decision comes down less to salary math and more to environment: defense-contractor onsite work versus cloud-native distributed teams. Explore live Systems Engineer and Cloud Architect openings on InterviewStack.io to see where real demand is in your market right now.

Data Scientist vs Machine Learning Engineer 2026: The $43K Premium

Gnana — Sun, 28 Jun 2026 17:49:13 +0000

Two Roles Share Half a Stack. One Earns $43K More.

Machine Learning Engineers earn $194,300 at the US median. Data Scientists earn $151,500. That is a $42,800 gap between two roles that share exactly half their skill set: a Jaccard overlap of 0.50 across each role's top-30 skills. When two careers share that much ground, similar pay is the expected outcome. These don't follow that pattern.

The explanation lives in the half that does not overlap. Among 7,317 active Data Scientist postings and 4,718 active Machine Learning Engineer postings on InterviewStack.io as of June 2026, the data draws a clean line: Data Scientists own the analysis and insight layer; ML Engineers own the deployment and production layer. Companies pay a premium to keep models running in production under real-world load, not just to build them.

	Data Scientist	Machine Learning Engineer
Median US base salary	$151,500	$194,300
Active postings	7,317	4,718
Skill overlap (Jaccard)	50% shared	(pairwise)
Top skill	Python (63%)	Machine Learning (71%)
Remote share	17%	24%
Entry-level share	6.9%	4.6%

Key Findings

$42,800 salary gap: Machine Learning Engineers earn a median $194,300 US base salary versus $151,500 for Data Scientists (n=1,651 DS, n=1,249 MLE; base salary only, equity and bonus excluded).

50% skill overlap (Jaccard 0.50): Python and Machine Learning are near-universal in both roles; the split happens below that surface.

SQL divides the roles: 46% of Data Scientist postings require SQL versus 17% for Machine Learning Engineers, one of the sharpest divergences across the two skill profiles (PyTorch shows an equivalent gap in the opposite direction: 13% DS vs 42% MLE).

Production engineering is the MLE premium: CI/CD (24%), Kubernetes (19%), Docker (18%), and MLOps (27%) are exclusive or heavily skewed to ML Engineers.

Data Scientists have 55% more job openings: 7,317 active postings versus 4,718 for MLE, a meaningful volume advantage.

Entry-level access differs modestly: 6.9% of DS postings are explicitly entry-level versus 4.6% for MLE; neither role is easy to break into.

MLEs work more remotely: 24% remote share versus 17% for Data Scientists.

What Do Data Scientists and Machine Learning Engineers Actually Do?

Data Scientists work on the investigation layer. They build hypotheses, query data to test them, run statistical models, and translate findings into decisions or forecasts. A typical week might involve designing an A/B test, validating its significance, prototyping a forecasting model in Python, and presenting the results to a product or business team. The outputs are notebooks, reports, slide decks, or a model handed off to engineering for production. Statistics (40% of DS postings), data visualization (29%), and SQL (46%) are all signals of this stakeholder-facing, analysis-first orientation.

Machine Learning Engineers are closer to software engineering than to research. They take models that data scientists or research teams built and make them run reliably at scale. A typical week might involve building CI/CD pipelines for model retraining, containerizing inference services with Docker and Kubernetes, debugging model drift in production, or fine-tuning a large language model for a specific downstream task. The output is a system that operates without someone watching it. Notably, "Data Science" appears as an explicit requirement in 27% of MLE postings, meaning employers expect MLEs to understand the full modeling lifecycle, not just the infrastructure side.

Which Skills Do Both Roles Share?

Both roles are grounded in Python, Machine Learning, cloud platforms, and algorithmic thinking. These show up at high frequency on both sides of the comparison.

Skill frequency in Data Scientist (emerald) and Machine Learning Engineer (slate) postings. Python and Machine Learning anchor both; SQL and production-engineering tools are where the profiles diverge.

Python is essentially universal: 63% of DS postings and 66% of MLE postings list it. Machine Learning follows at 49% (DS) and 71% (MLE). AWS (20% DS, 33% MLE), monitoring (19% DS, 30% MLE), algorithms (21% DS, 24% MLE), TensorFlow (12% DS, 30% MLE), deep learning (11% DS, 27% MLE), data pipelines (20% DS, 22% MLE), and Generative AI (14% DS, 23% MLE) all clear 10% in both roles.

The 50% Jaccard is the highest overlap we have seen in the series for roles with a salary gap this large. The shared foundation is real. What you do with that Python and ML knowledge is what separates the compensation.

Where the Stack Splits

The divergence is clean at the exclusive skill level. Data Scientists lean toward analysis and communication tools; ML Engineers lean toward deployment and infrastructure.

Data Scientist exclusives (appear in DS postings; absent or below threshold in MLE):

Skill	DS Frequency
Data Visualization	29%
Data Quality	16%
Power BI	15%
Tableau	14%
Forecasting	11%
pandas	11%
Excel	10%

Data Visualization (29%), Power BI (15%), and Tableau (14%) are presentation-layer tools that surface findings to non-technical stakeholders. Forecasting (11%) and pandas (11%) are data manipulation and prediction tools that stay inside the notebook. Statistics, shared but heavily weighted toward DS at 40% vs 14% for MLE, further signals the role's formal analytical orientation.

Machine Learning Engineer exclusives (appear in MLE postings; absent or below threshold in DS):

Skill	MLE Frequency
CI/CD	24%
Kubernetes	19%
Docker	18%
RAG	16%
Model Training (explicit)	16%
APIs	16%
Scalability	14%
Fine Tuning	13%

CI/CD, Kubernetes, and Docker are the software-engineering backbone of production ML. RAG (retrieval-augmented generation) and fine-tuning are applied GenAI engineering skills: building production AI pipelines, not experimenting in notebooks. If you want to work in roles that explicitly require MLOps and model deployment, the MLE track is the direct path.

One framing worth noting on the GenAI numbers: job postings cite AI skills when they are explicit requirements, meaning someone hired specifically to build AI systems. The figures here (23% Generative AI in MLE, 14% in DS) measure that builder layer. Both roles sit well inside the ambient layer: the Stack Overflow Developer Survey and similar developer reports consistently show the large majority of working engineers now use AI coding tools weekly, regardless of whether "AI" appears in their job title. GitHub Copilot, Claude for code, and ChatGPT are baseline tools for both roles now. The posting percentages tell you who is architecting the AI pipelines, not who is using Copilot to write code.

Which Pays More, and Why?

All salary figures below are US-only base salary from postings with disclosed compensation. Equity, bonuses, and sign-on are excluded; total compensation at top employers runs meaningfully higher than these numbers.

Machine Learning Engineers earn $194,300 at the US median versus $151,500 for Data Scientists, a $42,800 gap (28% premium). The premium reflects the engineering layer of the MLE role, visible in which skills carry the highest pay within each role.

US base salary medians for both roles. MLE salaries start $42,800 higher at the baseline; the gap between roles for the same shared skill (Python: $155K DS vs $193K MLE) reflects the overall baseline difference, not a skill-specific premium.

Top-paying MLE skills (all above the $194,300 MLE baseline):

Skill	Median US Salary	Sample Size
RLHF	$235,600	39
JAX	$227,500	101
Distributed Training	$221,300	94
ONNX	$220,000	44
Fine Tuning	$208,600	184
CUDA	$208,600	86
Reinforcement Learning	$207,500	149
C++	$204,500	224

RLHF (reinforcement learning from human feedback, the alignment technique behind modern LLMs) and JAX (Google's high-performance ML framework) sit well above the MLE baseline. Distributed Training, ONNX (a model portability and optimization format), and CUDA (GPU programming for neural networks) all cluster around $220K, signaling that training large models at infrastructure scale is among the highest-paid work in the field.

Top-paying DS skills (above the $151,500 DS baseline):

Skill	Median US Salary	Sample Size
Causal Inference	$194,300	163
A/B Testing	$183,000	349
MLOps	$169,500	115
Vector Databases	$169,100	38
Feature Engineering	$165,300	140
Deep Learning	$163,000	150

Causal inference is the signal skill here: a Data Scientist who masters experiment design and causal methodology reaches the MLE median ($194,300 for causal inference vs $194,300 MLE baseline). That skill set routes into product data science roles at tech companies, the highest-value DS context in the market. A/B testing at $183,000 (n=349) confirms the same pattern: experimentation fluency at scale is what the top of the DS salary distribution looks like.

Which Role Has More Open Positions?

Data Scientists: 7,317 active postings. Machine Learning Engineers: 4,718. The DS market is 55% larger by volume, a meaningful hiring pool advantage. (The Data Scientist dataset spans a broad set of data-adjacent postings, including data architects, research analysts, and academic research roles, which modestly inflates the raw DS count relative to a strictly bounded definition.)

Seniority mix is nearly parallel. DS is modestly more accessible to career switchers: 6.9% entry-level share versus 4.6% for MLE. In both cases, mid-level dominates at 55% (DS) and 52% (MLE), with senior and staff roles accounting for 38% (DS) and 43% (MLE) of the market. Neither role has a deep junior pipeline; most postings expect you to arrive with production Python and ML experience already in place.

The US anchors both markets: 38% of DS postings and 45% of MLE postings originate in the US. Data Scientist spreads more broadly internationally: India (11%), UK (5%), Germany (3%), and Singapore (3%) all have meaningful share, consistent with the role's broader industry footprint. ML Engineer is more concentrated in North America and strong in India (12%) and Canada (5%).

On remote access: ML Engineers are modestly more remote-friendly at 24% versus 17% for Data Scientists, with both roles predominantly onsite (55% DS, 54% MLE) or hybrid (32% DS, 30% MLE). Note that postings can carry multiple work-mode tags, so these figures sum above 100%.

Which Role Should You Choose?

Choose Data Scientist if you:

Come from a statistics, economics, or quantitative research background
Want to work directly with business stakeholders to shape decisions through data analysis and modeling
Are more interested in developing, testing, and communicating models than in deploying infrastructure
Need a larger job pool to get started: 7,317 active openings versus 4,718 for MLE
Are comfortable with SQL as a core daily tool (46% of DS postings require it)

Choose Machine Learning Engineer if you:

Have software engineering experience and are comfortable with CI/CD, containers, or distributed systems
Want to build the production systems that serve ML predictions at scale
Are ready to invest in MLOps, Kubernetes, and deployment tooling beyond the model itself
Are targeting the AI/LLM infrastructure space (RAG, fine-tuning, and LLMs are MLE-skewed skills)
Want the $42,800 salary premium that production-engineering skills command

The transition from DS to MLE is well-documented. MLOps appears in 10% of DS postings and 27% of MLE postings; building familiarity with CI/CD and containerization is the clearest signal that you are ready to cross. If you are already a DS who has deployed models to production, the gap is smaller than the salary figures imply.

Browse open Data Scientist roles or Machine Learning Engineer roles on InterviewStack.io to see exactly what today's postings are asking for, filtered to remote or hybrid if flexibility is a priority.

FAQ

Q. What is the median US salary for a Data Scientist vs Machine Learning Engineer in 2026?

Among US postings with disclosed salary, Data Scientists earn a median of $151,500 (n=1,651) and Machine Learning Engineers earn $194,300 (n=1,249), a $42,800 gap. These are base salaries only; equity and bonuses are not reflected in job-posting data.

Q. What skills do Data Scientists and Machine Learning Engineers share?

Both roles share Python (63% DS, 66% MLE), Machine Learning (49% DS, 71% MLE), AWS, monitoring, algorithms, and deep learning. The Jaccard similarity across their top-30 skill sets is 0.50; half the skills overlap, but frequency and emphasis diverge significantly.

Q. Which role is easier to break into as an entry-level candidate?

Data Scientist is modestly more accessible: 6.9% of its 7,317 postings are entry-level, versus 4.6% of Machine Learning Engineer's 4,718 postings. Neither role is easy to enter; most postings for both expect experience with ML workflows and Python.

Q. What skills does a Machine Learning Engineer need that a Data Scientist doesn't?

MLEs need production engineering skills that rarely appear in Data Scientist postings: CI/CD (24%), Kubernetes (19%), Docker (18%), RAG (16%), Model Training (16%), fine-tuning (13%), and scalability engineering. These infrastructure skills are what employers pay the $43K premium for.

Q. Is SQL important for Machine Learning Engineers?

Not by job-posting frequency. SQL appears in 46% of Data Scientist postings but only 17% of Machine Learning Engineer postings, one of the sharpest divergences across the two skill sets (PyTorch shows a near-identical gap in the opposite direction at 13% DS vs 42% MLE). MLEs work with model pipelines and production infrastructure more than relational databases.

Q. Which role has more job openings in 2026?

Data Scientist has significantly more: 7,317 active postings versus 4,718 for Machine Learning Engineer, a 1.55x volume advantage. The US accounts for 38% of Data Scientist postings and 45% of Machine Learning Engineer postings.

Q. Should I become a Data Scientist or Machine Learning Engineer?

Choose Data Scientist if you have a statistics or analytics background and want to influence business decisions through modeling and insight generation. Choose Machine Learning Engineer if you have software engineering experience and want to build the systems that deploy and maintain ML models in production. The $43K salary gap is real, but so is the engineering bar to clear it.

Before You Pick a Path

Both roles are healthy and growing. Data Science has more openings and broader global reach; ML Engineering commands a $42,800 salary premium with a tighter focus on production infrastructure. The shared Python and Machine Learning foundation means these careers are not as divergent as the salary gap implies, but the gap is real, and it sits entirely in the engineering half of the MLE stack, not the ML half that both roles already share.

To prepare for either path, the InterviewStack.io question bank covers ML fundamentals, algorithms, statistical modeling, and system design for both roles. AI mock interviews let you practice the scenario-based questions each role attracts under realistic interview pressure. And interactive courses can help you close the gap on whichever skills sit between where you are now and the role you are targeting, whether that is MLOps and distributed systems for the MLE track or causal inference and experimentation design for the DS ceiling.

UI Designer Skills in 2026: The $15K Adobe Penalty

Gnana — Sat, 27 Jun 2026 22:45:57 +0000

Figma Is Now the Baseline, Not the Bar

For most of the past decade, knowing Figma gave you a real edge in a UI Designer application. In 2026, it gets you into the room.

Of the 937 active UI Designer postings analyzed on the InterviewStack.io job board in June 2026, Figma appears in 60.5%, the only skill to clear the 50% table-stakes threshold. That majority-tier presence carries a consequence: it barely moves salary. Figma earns just $2,200 above the $105,300 US median. Adobe Creative Suite, present in 26.6% of postings, earns $15,300 below it. The distinction matters because the tool you lead with signals which segment of the UI Designer market you are targeting, and those segments pay very differently.

Key Findings

937 active UI Designer postings analyzed as of June 2026.

Figma is the only table-stakes skill at 60.5%, the sole skill clearing the 50% threshold. No other individual skill comes close.

US median base salary: $105,300 (n=124 postings with US salary disclosed). All salary figures are US base only; equity and bonuses are excluded.

Adobe Creative Suite (26.6% of postings) pays $90,000 US median, $15,300 below the $105,300 baseline.

Design Systems (35.1% of postings) pays $115,200 US median, the top premium at +$9,900 above baseline.

CSS and HTML carry the highest co-occurrence lift in the dataset at 5.59: when one appears in a posting, the other nearly always follows, marking a distinct designer-who-codes segment.

Entry-level is 7.4% (69 of 937 postings); mid-level dominates at 64%.

49% of postings are onsite, 28% remote, less flexible than most software engineering roles.

Dataset note: These 937 postings include roles classified as UI Designer alongside adjacent titles such as Visual Designer, Digital Designer, and Web Designer. A small proportion appear to represent retail visual merchandising positions rather than digital product UI design; this modestly affects skill frequencies for marketing-adjacent terms and salary figures for print/brand-heavy tools like Adobe Creative Suite.

Which Skills Pay Above the $105K Baseline?

Among US postings with disclosed salary (n=124), the divide between skill clusters is stark. All figures below are US base salary only, derived from posting disclosures. Total compensation at top employers is higher once equity and bonuses are counted.

Base salary by skill for UI Designer roles (global-mixed figures shown for broader sample size; US-only medians discussed in text). Skills with fewer than 25 US salary data points are excluded.

At the top of the salary table, three related skills cluster together: Design Systems ($115,200), UI Design proficiency ($113,100), and User Experience fluency ($111,900). These are not separate tools: they signal a designer who thinks in systems and interaction patterns rather than individual visual decisions. Design Systems is $9,900 above the $105,300 baseline, and it appears in 35% of postings. Meaningful demand plus a real premium is what defines a salary lever.

Figma ($107,500), Visual Design ($107,500), Prototyping ($107,500), and Accessibility ($107,500) all cluster just above baseline, each earning roughly $2,200 in premium. These are expected competencies: enough candidates have them that they no longer move pay on their own.

The clearest negative signal: Adobe Creative Suite. At $90,000 US median, it pays $15,300 below the $105,300 baseline despite appearing in more than a quarter of postings. This is not a data anomaly. Adobe Creative Suite in a UI Designer posting typically tracks brand, print, or marketing work: roles that sit in a lower pay band than product UI design. If your portfolio centers on Adobe tools, you are most likely competing in that segment, and the $15,300 gap is structural, not incidental.

Typography ($101,300) also sits below baseline at -$4,000, reinforcing the same pattern: pure visual craft skills are expected without earning a premium in the product UI segment.

Skill	US Median	vs. Baseline	Sample
Design Systems	$115,200	+$9,900	n=44
UI Design	$113,100	+$7,800	n=28
User Experience	$111,900	+$6,600	n=32
Product Strategy	$109,200	+$3,900	n=33
Figma	$107,500	+$2,200	n=71
Visual Design	$107,500	+$2,200	n=53
Prototyping	$107,500	+$2,200	n=30
Accessibility	$107,500	+$2,200	n=30
Storytelling	$106,900	+$1,600	n=40
Role baseline	$105,300		n=124
Typography	$101,300	-$4,000	n=46
Adobe Creative Suite	$90,000	-$15,300	n=40

The practical read: listing Figma gets you interviews; demonstrating Design Systems ownership and UX rigor gets you toward the top of the pay range.

The UI Designer Skill Stack

Group individual skills by family and the role's structure comes into focus. Visual and UX design skills are the core of nearly every posting. The secondary signals reveal what kind of team you are joining.

Share of UI Designer postings that ask for at least one skill in each family. A posting that mentions both Figma and Adobe Creative Suite counts once under the design-core group.

Because the analytics taxonomy groups most visual and UX skills into a single broad bucket, the chart concentrates at the top. The interesting signal is in the secondary families:

Process & Methodology at 22.8%: Agile appears in 15% of postings, Project Management in 6%. Nearly 1 in 4 postings expects some cross-functional process fluency.
Statistics & Experimentation at 12.7%: Almost entirely A/B Testing. About 1 in 8 UI Designer roles expects the designer to participate in experiment design or interpret test results, a signal of product-driven, data-informed teams.
Coding Languages at 8.0%: Largely JavaScript at 6.6%. A meaningful minority of roles expect light coding, particularly at agencies and startups.
Machine Learning & AI at 3.7%: The explicit AI requirement is low (35 of 937 postings). But that 3.7% measures only roles where building or integrating AI capabilities is the deliverable, not roles where using AI tools is simply assumed.

On that last point: the AI in Design 2026 Report (906 designers across 60+ countries, by Designer Fund and Foundation Capital) found that 91% of designers now use AI tools and 75% do so every single day, up from 54% weekly users in 2025. The 3.7% explicit mention rate does not mean 96% of UI Designer roles skip AI. It means 96% of postings treat AI tool use as a given rather than a qualification to screen for. Figma, the top required skill, ships AI features directly in the canvas. A designer working in Figma in 2026 is almost certainly using its AI capabilities too. The explicit adoption figure is a floor on who is building AI products, not a ceiling on who is using AI in their daily workflow.

Which Skills Are Expected vs. Which Stand Out?

Three tiers structure the landscape.

Individual UI Designer skills by share of postings. Table stakes (50%+): expected on every competitive application. Common (20-50%): expected on many roles. Differentiator (5-20%): meaningful signal, not universal.

Table Stakes (50%+ of postings)

One skill occupies this tier: Figma at 60.5%. Three of every five postings require it. If your toolkit is Adobe-only, this is the most immediate gap to address before applying to product UI roles.

Common Expectations (20-50% of postings)

Seven skills sit in the common tier. None is required on every role, but you should be able to credibly claim three to four on any given application:

Design Systems: 35.1% (top salary earner)
Typography: 30.6% (expected but below-baseline salary signal)
Visual Design: 28.7%
Adobe Creative Suite: 26.6% (penalty-zone pay)
User Experience: 26.4%
Product Strategy: 24.4%
Wireframes: 21.3%

Differentiators (5-20% of postings)

A wide set of skills separates candidates: Prototyping (19.6%), Storytelling (19.3%), Accessibility (19.2%), HTML (16.8%), CSS (15.0%), Agile (14.9%), User Research (13.3%), Interaction Design (12.5%), A/B Testing (12.1%), Sketch (12.0%), Motion Design (11.2%), and Branding (11.1%).

Accessibility is worth singling out in this tier. At 19.2% of postings, it earns $107,500 US median, matching the salary of Figma (60.5%) despite appearing in less than a third as many postings. The companies requiring Accessibility as an explicit skill are paying the same rate as those requiring the industry's most universal tool. They are different markets with aligned compensation. For a candidate who genuinely specializes in accessible design, that convergence is a real opportunity.

The Coding Niche Inside UI Design

The most striking statistical fact in this dataset is not a salary number. It is a co-occurrence lift.

CSS and HTML together carry a lift of 5.59, the highest pair in the entire analysis. Lift measures how much more often two skills appear together than chance would predict. A lift of 5.59 means the pair co-occurs 5.59 times more often than their individual frequencies alone would suggest. In practice: when a UI Designer posting mentions CSS, HTML almost always appears alongside it. The two are treated as a single package, not independent skills.

Skill Pair	Co-occurrence	Lift	Signal
CSS + HTML	14.1% of postings	5.59	Designer-who-codes package
UI Design + Wireframes	10.1%	2.47	Structured, process-driven design
User Experience + Wireframes	11.2%	1.99	UX-to-delivery workflow
Accessibility + Design Systems	12.3%	1.82	Systems + inclusive design
Design Systems + Figma	30.2%	1.42	Core product UI stack

The CSS + HTML block defines a discrete segment: UI Designers who bridge design and frontend implementation. These roles appear most often at agencies, startups, and companies without dedicated frontend engineers where the designer is also expected to prototype or deliver production-ready markup. JavaScript at 6.6% extends the coding expectation further on some postings.

The next-highest pair, UI Design + Wireframes at lift 2.47, captures a different signal: structured, process-visible design work where wireframing is a delivered artifact rather than a mental step. Companies requiring both tend to want to see the thinking, not just the final visual.

Design Systems + Figma at 30.2% co-occurrence and lift 1.42 forms the dominant product UI stack, present in nearly 3 in 10 postings. This is the core credential for product-tier UI work.

How Hard Is It to Break Into UI Design?

The entry gate is narrower than most candidates assume, but wider than in pure engineering roles.

Seniority distribution across 937 UI Designer postings. Mid-level is the dominant tier; entry-level is 7.4%.

Entry-level is 7.4% of the market (69 of 937 postings). That is meaningfully more accessible than Cloud Architect (under 1%), Security Architect (under 2%), or Test Automation Engineer (2%), but well below what candidates tend to assume. Mid-level dominates at 64%, senior at 24%.

The portfolio matters more in this role than in most. Hiring managers can evaluate the work directly before reading the resume. A candidate with four or five thoughtful case studies showing process, iteration, and shipped Figma deliverables will outcompete a mid-level candidate with a stale or thin portfolio. Entry-level UI Designer postings routinely expect portfolio links alongside the application.

Staff-level is just 4.1%, a thin ceiling compared with software engineering, where the staff and principal IC track extends meaningfully. UI design tends to top out at senior or design lead rather than building a deep individual contributor ladder.

Where UI Designers Work

The US accounts for 28.6% of postings (268 of 937), making this one of the more globally distributed design roles on the board. Canada (6.4%), India (5.7%), Germany (4.9%), and the UK (4.6%) each have a real presence. For a design discipline that can often be executed anywhere with a screen and internet access, the geographic spread is notable.

Geographic distribution of 937 active UI Designer postings. Postings with unknown location (12%) are excluded from the chart.

Work mode split across all 937 postings. Onsite is the plurality despite the role's remote-compatible nature.

On work mode: 49% onsite, 28% remote, 27% hybrid. (Note: some postings are tagged with more than one work mode, so percentages sum above 100%.) The onsite share is higher than the role's remote compatibility might suggest. Design reviews, cross-functional whiteboarding, and feedback loops with PMs and engineers appear to drive a preference for proximity even at companies that could support remote design work. If remote-first matters to you, filtering to remote UI Designer postings surfaces 265 active openings.

Who's Hiring

Agencies, technology services, and enterprise employers lead the roster: a cross-industry mix that reflects how broadly digital UI design has diffused.

Top UI Designer employers by distinct openings. Staffing firms, design marketplaces, and retail-sector employers whose postings appear to be in-store visual merchandising roles rather than digital UI design are excluded.

Company	Postings	Sector
VML	19	Creative and marketing agency
Steampunk	14	Technology services
Accenture	12	Consulting
Air Apps	10	Software
General Motors	9	Automotive
Buck Mason	7	Apparel (ecommerce)
Prolific	7	Research platform
Bamboo Works	7	Financial media
LBG	6	Financial services
Konrad Group	6	Digital agency
DEPT	5	Digital agency
FYST	5	Fintech

Creative agencies (VML, Konrad Group, DEPT) dominate the top, alongside enterprise and technology employers (Accenture, Steampunk, General Motors, LBG) and digital product companies (Air Apps, Prolific). The sector spread aligns with what the skill data shows: UI design is a discipline that every industry building a digital product or interface now hires for.

How to Use This in Your Job Search

Mock Interviews. The portfolio walkthrough opens most UI Designer interviews, but process and judgment questions follow: how you handled a conflicting stakeholder review, how you approached an accessibility problem, how you built or contributed to a design system. Practice these scenarios in context with AI mock interviews before the real conversation.

Question Bank. Design Systems, Interaction Design, Accessibility, and A/B Testing surface repeatedly across UI Designer interviews. The question bank covers these topics for design roles and helps you prepare concrete, specific answers rather than generic process descriptions.

Courses. If your Figma skills are solid but your Design Systems fluency is thin, that is the highest-leverage gap to close: it commands the top salary premium in the dataset. Our interactive courses cover design systems, accessibility, and product thinking fundamentals.

Job Board. Browse all active UI Designer postings, or narrow to the segment you are targeting: Design Systems roles, remote openings, or entry-level positions.

FAQ

Q. What is the median salary for a UI Designer in 2026?

The median US base salary across 124 UI Designer postings with disclosed salary data is $105,300. All figures are base only; equity and bonuses are not captured in posting data. Globally-mixed figures (n=172) produce a lower median of $94,400 due to international market mixing.

Q. What skills do companies require for UI Designer roles in 2026?

Figma is the only table-stakes skill, appearing in 60.5% of postings. The common tier (20-50%) includes Design Systems (35%), Typography (31%), Visual Design (29%), Adobe Creative Suite (27%), User Experience (26%), Product Strategy (24%), and Wireframes (21%). Differentiators below 20% include HTML, CSS, Agile, Prototyping, A/B Testing, Storytelling, and Interaction Design.

Q. Which UI Designer skills pay the most in 2026?

Design Systems leads at $115,200 US median, $9,900 above the $105,300 baseline. UI Design proficiency earns $113,100 (+$7,800) and User Experience $111,900 (+$6,600). At the other end, Adobe Creative Suite pays $90,000, which is $15,300 below baseline, signaling a different, lower-compensated market segment.

Q. How much does Figma knowledge affect UI Designer salary?

Figma is required in 60.5% of postings but earns a US median of $107,500, just $2,200 above the $105,300 baseline. Because Figma is now table stakes, listing it does not meaningfully differentiate a candidate. The salary signal comes from what sits above Figma: Design Systems, UX fluency, and accessibility.

Q. How hard is it to get an entry-level UI Designer job?

7.4% of UI Designer postings (69 of 937) are explicitly entry-level. Mid-level roles dominate at 64%. The role is more accessible than Security Architect or Cloud Architect (both under 2% entry-level), but a strong portfolio of shipped digital work is expected even for junior positions.

Q. Is UI Designer a remote-friendly role in 2026?

Modestly. 28% of postings (265 of 937) are tagged remote, with 27% hybrid and 49% onsite. UI design is more remote-accessible than embedded engineering or test automation, but onsite and hybrid together account for three-quarters of openings.

Q. Which companies hire the most UI Designers in 2026?

Top digital UI design employers include VML (19 postings), Steampunk (14), Accenture (12), Air Apps (10), General Motors (9), and Buck Mason (7). The mix spans creative agencies, technology services, and enterprise employers. Note: some postings in this dataset come from retail apparel brands whose listings appear to be in-store visual merchandising roles rather than digital UI design positions and are excluded from this table.

What the Tool Stack Is Actually Telling You

The UI Designer market in 2026 is shaped by one central fact: Figma has become infrastructure. It appears in 3 of 5 postings and earns a $2,200 premium, nearly invisible in salary terms. The differentiation has shifted entirely to what you build with Figma. Design Systems ownership (+$9,900) and UI Design fluency (+$7,800) sit $7,000 or more above baseline. UX rigor adds $6,600 and accessible component thinking adds $2,200: meaningful gains, but in a different tier from the top earners. Adobe Creative Suite sits $15,300 below it.

That gap is a market map. Product UI design and brand or print design are paying differently, and the skills you emphasize in your portfolio and resume will route you into one or the other. For candidates positioned toward product UI work, the signal in the pairs data is also worth noting: CSS and HTML co-occur at a lift of 5.59, the highest in the dataset. The designer-who-codes subset is a real and distinct niche, not a resume novelty. The combination of systems thinking, interaction fluency, and even light markup capability is where the role appears to be growing most distinctly in 2026.