DEV Community: Alex Bell

Data from 44,000 Interview Practice Sessions Shows Which Tech Companies Are Actually the Hardest

Alex Bell — Wed, 17 Jun 2026 20:02:38 +0000

Most "hardest tech company interviews" rankings are based on Reddit opinions, Glassdoor reviews, or surveys of a few hundred people. Final Round AI published a different kind of analysis.

Final Round AI tracks interview practice sessions across every major tech company. They analyzed 44,808 sessions from January 2023 through May 2025, calculated average practice scores for each company, and ranked them by actual performance data.

The results challenge what most people assume.

Meta Has the Lowest Average Practice Score of Any FAANG Company

Meta candidates average 55.5 out of 100 across 3,220 sessions, the lowest of any traditional FAANG company in our dataset. Here is how the full ranking looks:

Salesforce: 50.7 (1,062 sessions)
Meta: 55.5 (3,220 sessions)
TikTok: 55.5 (770 sessions)
Stripe: 55.6 (236 sessions)
Apple: 56.3 (4,528 sessions)
Google: 56.8 (16,604 sessions)
Amazon: 57.5 (18,932 sessions)
Nvidia: 58.2 (560 sessions)
Netflix: 59.2 (280 sessions)

Amazon has the most practice sessions by far and the highest FAANG average score. The most feared interview in tech is not the hardest to score on in practice.

Meta Got 11 Points Harder From 2024 to 2025

The year-over-year shift is where things get interesting for anyone currently prepping:

Meta: 56.5 in 2024 → 50.8 in 2025 (steepest decline of any company)
Amazon: 58.5 in 2024 → 55.2 in 2025
Google: 56.9 in 2024 → 55.8 in 2025
Apple: 56.0 in 2024 → 58.7 in 2025 (only major company that improved)

If you are prepping for Meta using resources from two years ago, you are likely under-prepared for what interviewers are asking today.

Why Amazon Scores Higher Despite Being "Harder"

Amazon has 18,932 sessions, more than Google and Apple combined. The depth of dedicated Amazon prep content (books, courses, communities built around the 16 Leadership Principles) shifts the baseline score upward. More candidates prepare more thoroughly for Amazon than for any other company.

The practical implication: fear of Amazon is well-calibrated for format complexity. But structured STAR-method preparation for Amazon actually works, and the data shows it.

TikTok and Stripe Are As Hard As Meta, With Far Less Prep Content

TikTok (55.5 across 770 sessions) and Stripe (55.6 across 236 sessions) sit right alongside Meta in difficulty. Both receive a fraction of the dedicated prep attention that FAANG companies get. If you are targeting either company, treat the prep with the same depth as a FAANG interview, not as an easier alternative.

Methodology

All data is aggregated and anonymized across real practice sessions on Final Round AI. Scores are automated evaluations of practice responses across relevance, structure, specificity, and completeness. No individual user data is included. Companies with fewer than 100 sessions were excluded.

Full report with charts: finalroundai.com/blog/hardest-tech-company-interviews

Curious what patterns others have seen. Does Meta being harder than Google match your experience preparing for both?

I went into my OpenAI SWE loop prepared for system design. The values round is what tripped me up.

Alex Bell — Thu, 11 Jun 2026 05:51:59 +0000

I had done the prep. Distributed systems, API design, a few weeks of LeetCode mediums. My coding and system design rounds went reasonably well.

The values and mission alignment round was where I lost points.

I had heard there would be a behavioral component, so I built out the usual STAR stories: conflict resolution, influence without authority, a time I failed. Standard prep. What I did not prepare for was a question that had nothing to do with execution quality.

The question: "Tell me about a specific time when you made a decision where short-term product velocity had to trade off against a longer-term safety or reliability concern. How did you weigh those?"

They wanted a real story. Not a philosophical take on AI safety. Not a general statement about valuing responsible development. A real moment where you had concrete pressure to move fast and chose to slow down because of a safety or reliability concern.

I had technical tradeoff stories. None of them had safety or reliability as the explicit constraint. I reconstructed something on the spot and it did not land well.

What I missed in my prep

Most behavioral prep guides treat all tech companies as roughly equivalent on the behavioral axis. STAR method, leadership principles, impact at scale. That framing works for Amazon or Google. It misses what OpenAI actually cares about.

The hiring bar there has a specific dimension that other FAANG behavioral rounds do not emphasize: how do you reason when safety and speed conflict and the right answer is not obvious?

You need stories where:

The safety or reliability concern was real and specific (not abstract)
You were the one who surfaced it or made the call to slow down
There was actual pressure from the other direction
You can articulate how you weighed the tradeoff, not just that you made the right choice

How to build the right stories

Go back through your work history and look for moments where:

You flagged a reliability issue before launch that delayed a feature
You pushed back on a timeline because the safety case was not solid
You made a decision with incomplete information where one path had higher downside risk

If you have those stories, reframe them explicitly around the safety/reliability constraint. Make that the center of the story, not just a detail.

If you do not have obvious examples, look harder. Most engineers who have shipped real systems have at least one moment where they chose caution. It may not feel like a big story. It does not need to be.

The framing that works

Do not open with your conclusion. Open with the pressure you were under to move fast. Then explain what you saw that made you pause. Then the decision. Then the outcome.

The interviewer wants to see the reasoning, not just the result.

There is an active discussion on this in the Final Round AI community with people comparing their own OpenAI loop experiences: https://www.finalroundai.com/community/t/openai-swe-behavioral-round-2026-the-values-and-mission-alignment-question-is-harder-than-it-sounds/87

For broader FAANG behavioral prep structure, the FAANG behavioral interview guide covers the STAR framework well. Just add the safety/reliability layer on top for OpenAI specifically.

Anyone else gone through an OpenAI loop recently? Curious how much variation there is across teams on this type of question.

Microsoft SWE Interview in 2026: What Changed and the Prep Plan That Works

Alex Bell — Wed, 10 Jun 2026 17:57:47 +0000

Microsoft is hiring SWE talent at a pace not seen in years. In early 2026, internal job postings for software engineers are up roughly 62% compared to the same period in 2024, driven almost entirely by Azure expansion and the company's aggressive push into AI infrastructure. If you have been waiting for a good time to target Microsoft, this is it.

Why Microsoft Right Now

Azure is the #2 cloud platform globally and growing faster than AWS in enterprise accounts. Microsoft is also embedding AI across every product line, from GitHub Copilot to Teams to Bing. That means the teams hiring right now are not just looking for general backend engineers. They want people who can reason about distributed systems, cloud-native design, and increasingly, AI/ML pipelines.

The competition is real, but the volume of open roles means more hiring loops are running simultaneously. Preparation quality matters more than timing.

The Interview Loop Structure

The Microsoft SWE loop has not changed dramatically in format, but the content inside each round has shifted. Here is what a standard loop looks like in 2026:

Recruiter Screen (30 min): Resume walkthrough, basic motivation questions, timeline check. No technical content.

Online Assessment (90 min): Two LeetCode-style coding problems on their internal platform. Difficulty ranges from medium to hard. You get test cases but not the hidden ones.

Virtual Onsite (4-5 rounds, same day or split over two days):

2 coding rounds (medium-hard, data structures and algorithms)
1 system design round
1 behavioral round (often with a senior engineer or manager)
Sometimes a 5th "as-appropriate" round focused on a technical deep dive

Each round is 45 to 60 minutes. The coding rounds expect clean, bug-free code with time and space complexity discussion.

What Changed Since 2024

Two shifts stand out.

More AI/ML questions in coding rounds. You no longer get through a Microsoft loop without seeing at least one problem involving embeddings, tokenization logic, or working with model outputs. These are not deep ML theory questions. They are practical coding problems that assume you understand what an LLM pipeline looks like end to end.

System design is now cloud-aware. In 2024, you could describe a distributed cache or message queue in provider-agnostic terms and do fine. In 2026, interviewers want to hear you reference actual trade-offs between managed services, talk about latency characteristics of object storage vs block storage, and address multi-region failover as a default concern, not an afterthought.

A 6-Week Prep Plan That Works

Weeks 1 and 2: LeetCode foundations

Focus on arrays, strings, trees, graphs, and dynamic programming. Aim for 2 to 3 problems per day. On Microsoft's OA, speed matters because you have 90 minutes for two problems with no hints. Use the Microsoft-tagged problem list on LeetCode. Do not skip the medium-difficulty graph problems. They show up more than people expect.

Weeks 3 and 4: System design

Study the classic distributed systems patterns: consistent hashing, leader election, write-ahead logging, and read replicas. Then go one layer deeper into cloud-native specifics. Read about how Azure Blob Storage differs from S3 in terms of consistency guarantees. Practice designing a real-time collaborative editing system, a URL shortener with global distribution, and a notification service that handles 10 million users.

Weeks 5 and 6: Behavioral prep and mock loops

This is where most candidates lose ground. Microsoft takes behavioral assessment seriously. Every round has at least a few behavioral minutes even if it is technically labeled a coding round.

I used Final Round AI's mock interview tool to simulate the full loop, running back-to-back rounds with AI feedback on both my coding explanations and my behavioral answers. The biggest thing it surfaced was that I was giving behavioral responses that sounded fine in my head but were actually too vague when I heard them played back.

Behavioral Prep for Microsoft Specifically

Microsoft's culture centers on growth mindset, a concept Satya Nadella made central to the company's turnaround starting in 2014. In behavioral rounds, interviewers are actively listening for evidence that you learn from failure, seek feedback, and update your thinking when you get new information.

The worst thing you can do is give a story where you were right from the beginning. The best stories have a moment where you were wrong or incomplete, and you course-corrected because of something a teammate said or a result you did not expect.

Prepare three to four stories that show this pattern. Map them to the STAR format but leave room for the "what I learned" extension at the end. That extension is what separates strong performers from borderline ones in Microsoft's calibration process.

The Bottom Line

Microsoft in 2026 is a strong target with real volume and competitive compensation. The loop is predictable once you understand its structure. The candidates who struggle are the ones who prepare for 2024 Microsoft and walk into 2026 interviews without AI/ML fluency or cloud-aware system design vocabulary.

Six weeks of focused prep is enough. Start with the fundamentals, layer in cloud and AI context, and practice full loops with real-time feedback before you go live.

If you want to compare notes or share your own experience with the Microsoft interview loop, there is an active thread in the Final Round AI community where people are discussing exactly this: https://www.finalroundai.com/community/t/microsoft-swe-interview-in-2026-what-changed-and-how-to-actually-prepare/83 - worth a read if you are actively prepping.