I spent the last few months building a database of culture data for every major AI company — Glassdoor ratings, work-life balance scores, culture values, and real employee reviews. The dataset now covers 45 companies and 7,100+ open roles.
Some of the findings genuinely surprised me. Here’s what the data says.
For each of the 45 companies, I collected:
• Glassdoor overall rating (1-5 scale, verified review count)
• Work-life balance score (1-5 scale, from Glassdoor sub-ratings)
• Culture values — tagged from careers pages, engineering blogs, and reviews (remote, async, flat, ship-fast, deep-work, etc.)
• Employee review themes — recurring pros/cons from Glassdoor and Blind
• Open roles — fetched live from ATS APIs (Greenhouse, Ashby, Lever, Workable)
This isn’t a survey. It’s structured data pulled from real sources, updated daily.
Top 10 AI Companies by Glassdoor Rating
Company Glassdoor WLB Score Size Open Roles
-----------------------------------------------------------
Vast AI 5.0 4.5 ~30 10
Supabase 4.8 3.0 ~250 37
Perplexity AI 4.7 3.3 ~500 79
Linear 4.6 4.4 ~80 19
LangChain 4.6 4.0 ~230 95
Plaid 4.6 4.2 ~800 100
Runway 4.5 4.0 ~420 35
OpenAI 4.5 3.6 ~3,500 661
incident.io 4.5 4.1 ~140 27
Anthropic 4.4 3.7 ~1,500 446
Look at the WLB column. The highest-rated companies don’t necessarily have the best work-life balance. That’s the first surprise.
The Supabase Paradox: 4.8 Rating, 3.0 WLB
Supabase has the second-highest Glassdoor rating in the entire dataset — but the lowest work-life balance score among top-rated companies at 3.0/5.0. And yet: 100% of Glassdoor reviewers recommend it to a friend.
How? Self-selection. The people who join Supabase want intensity. They run “launch weeks” — concentrated shipping sprints where major features get announced daily. It’s exhausting. It’s also the reason many engineers joined.
One employee review captures it: “Heavy focus on launch weeks versus fundamentals of reliability and automation.”
Supabase isn’t a company where people tolerate the pace despite hating it. They love it because of the intensity. The 3.0 WLB score is a self-aware acknowledgment, not a red flag.
Figma: The Brand vs The Reality
Figma’s product is universally beloved. The engineering is genuinely world-class — WebAssembly canvas rendering, CRDT-inspired multiplayer, C++ compiled to WASM.
But the Glassdoor data tells a different story: 3.7/5.0 overall, 3.1 WLB, only 64% recommend.
Anonymous reviews from Blind are more blunt:
• “WLB is 996 on a lot of teams” (referring to 9am-9pm, 6 days/week)
• “Had 5 different managers in 2 years”
• “Could put your 120% in and work 60 hours and get a meets expectations”
The gap between Figma’s external brand (playful, inclusive, creative) and anonymous employee reviews (burnout, fear culture, manager churn) is the biggest disconnect in the dataset.
Companies Where WLB Actually Matches The Rating
These companies scored high on both Glassdoor rating AND work-life balance:
| Company | Glassdoor | WLB | What Makes Them Different |
|---|---|---|---|
| PostHog | 4.3 | 4.5 | No meetings, no deadlines, autonomous 2-person teams |
| Tailscale | 4.4 | 4.5 | Same pay worldwide, 26 weeks parental leave |
| Linear | 4.6 | 4.4 | 80-person team, opinionated product culture |
| Weaviate | 4.3 | 4.2 | Fully async, flat hierarchy, open source |
| Pinecone | 4.2 | 4.3 | Fully remote, flexible hours |
| Notion | 4.4 | 4.2 | Product-first culture, strong design DNA |
PostHog is the standout. Their handbook is public. They don’t have standup meetings. Engineers work in autonomous 2-3 person “small teams” with no deadlines. It reads like it shouldn’t work — but 4.3 Glassdoor and 4.5 WLB says it does.
The Worst WLB in AI
These companies scored below 3.5 on work-life balance:
| Company | Glassdoor | WLB | Why |
|---|---|---|---|
| Scale AI | 3.5 | 2.7 | "Extremely long hours are the norm" |
| Supabase | 4.8 | 3.0 | Launch week intensity (by choice) |
| Figma | 3.7 | 3.1 | Post-IPO pressure + manager churn |
| CoreWeave | 3.6 | 3.2 | GPU infrastructure scaling at breakneck speed |
| Perplexity AI | 4.7 | 3.3 | Startup pace with moonshot ambitions |
| Fireworks AI | 4.2 | 3.3 | Small team, huge technical scope |
| Vercel | 3.9 | 3.4 | "Ship or die" culture |
Scale AI at 2.7 WLB is the lowest in the dataset — and it shows in their 3.5 Glassdoor rating. Unlike Supabase, where people choose intensity, Scale AI reviews suggest the pace is imposed rather than embraced.
Culture Values That Predict Satisfaction
This is where it gets interesting. I tagged each company with culture values and looked at averages:
| Culture Value | Avg Glassdoor | Avg WLB | # Companies |
|---|---|---|---|
| Async Communication | 4.36 | 4.04 | 5 |
| Flat Hierarchy | 4.23 | 3.89 | 14 |
| Remote-First | 4.23 | 3.98 | 17 |
| Ship Fast | 4.15 | 3.62 | 27 |
The pattern: companies that value async communication and flat hierarchy consistently score higher on both satisfaction and WLB. Companies that prioritize “ship fast” have higher overall ratings but notably worse WLB.
Remote-first companies average 4.23 Glassdoor vs 4.11 for non-remote — a meaningful gap across 45 companies. The WLB difference is even larger: 3.98 vs 3.69.
Hidden Gems Most Engineers Haven’t Heard Of
If you’re only looking at Anthropic, OpenAI, and Google, you’re missing some of the best cultures in AI:
incident.io — 4.5 Glassdoor, 4.1 WLB. Incident response platform out of London. 13 extra holidays per year, 10% pension match, ego-free founders. All ex-Monzo engineers. 27 open roles.
Weaviate — 4.3 Glassdoor, 4.2 WLB. Open-source vector database. Fully async, flat hierarchy, distributed across Europe. Same pay regardless of location. Only 5 jobs open — they’re tiny and selective.
Modal — 4.0 Glassdoor, 3.8 WLB. Cloud infrastructure for AI. Founded by Erik Bernhardsson (ex-Spotify, built Luigi). 50x revenue growth. 27 open roles. If you want to work with elite systems engineers, this is it.
Baseten — 4.3 Glassdoor, 3.5 WLB. ML inference infrastructure. Small team (48 jobs), YC-backed, working on genuinely hard serving problems. Lower WLB but strong ratings suggest people are there by choice.
What I’d Tell Someone Evaluating an AI Company Offer
After building this dataset, here’s my framework:
1. Check the WLB score, not just the overall rating. Supabase has 4.8 overall but 3.0 WLB. That’s a company where people love the mission but acknowledge the cost. Know what you’re signing up for.
2. Look for the async/flat signal. Companies with these values consistently deliver better experiences. It’s not a coincidence.
3. Be skeptical of high ratings with few reviews. Vast AI has a perfect 5.0 — but with very few reviews. Linear at 4.6 is more statistically meaningful.
4. Read the Blind reviews, not just Glassdoor. Glassdoor reviews skew positive (companies sometimes encourage happy employees to review). Blind is anonymous and unfiltered. The delta between the two tells you a lot.
5. The “recommend to friend” percentage matters more than the overall score. Figma has a respectable 3.7 Glassdoor but only 64% recommend. Supabase has a lower WLB but 100% recommend. The recommend percentage captures something the aggregate score misses.
The Full Data
I’ve built all of this into an interactive tool where you can:
• Browse all 45 companies with ratings, values, and reviews: https://jobsbyculture.com/directory
• Compare any two companies side-by-side: https://jobsbyculture.com/compare
• Filter 7,100+ jobs by culture values like remote, async, flat hierarchy, deep work: https://jobsbyculture.com/jobs
The data updates daily from live ATS APIs. Happy to answer if you have any questions in the comments.
Top comments (0)