OpenAI vs Anthropic: Which Is Better to Work For?

Choosing between OpenAI vs Anthropic comes down to what you want from an AI lab right now, not which one wins a headline.

In my role leading Employer Partnerships & Strategy at Simplify, I spend a lot of time tracking where top technical talent goes, how companies compete for it, and what candidates are optimizing for when they evaluate opportunities. Over the last two years, no comparison has come up more often than OpenAI versus Anthropic.

The challenge is that most people approach the decision as if they’re comparing two versions of the same company. They’re not. They have different cultures, different compensation philosophies, different hiring processes, and different ideas about how AI should be built and deployed.

Both companies are now worth close to a trillion dollars. Anthropic raised $65B in May 2026 at a $965B valuation, edging past OpenAI's $852B from its March 2026 round. Those numbers are huge and a little meaningless for a job seeker. What matters is what they tell you about how you'd get paid, how you'd get hired, and what the day-to-day actually feels like.

How does pay and equity compare at OpenAI vs Anthropic?

This is the part most people get wrong, because they look at one number and stop.

On cash and total comp, OpenAI pays more. Per Levels.fyi data from December 2025, an OpenAI software engineer's median total comp was around $630K, with research scientists clearing seven figures. Anthropic's median total was closer to $545K, with base salaries in the $300K to $425K range. Top OpenAI researchers reportedly pull $10M+ with retention bonuses. So if you're optimizing for the biggest number today, OpenAI wins.

But equity at a private company is only worth what you can sell it for. OpenAI ran a $10.3B secondary share sale in August 2025 at a $500B valuation, which let employees cash out actual money before any IPO. Anthropic has not offered anything like that yet. OpenAI is also rumored to be targeting a September 2026 IPO window, which means a clearer path to liquidity, though a standard 180-day lockup applies after.

📝

Note: Treat private equity as illiquid until you see a concrete liquidity event. Ask each lab directly whether a secondary sale exists, who qualifies, and how often it has run.

So the real question is whether you want a higher number now with a clearer cash-out path at OpenAI, or you're betting that Anthropic's faster valuation climb, $380B in February to $965B in May 2026, means your shares grow more before you can sell. Neither is wrong. Just know which bet you're making before you sign.

How do OpenAI and Anthropic actually hire, and how should you prep?

If you're early-career, this section matters more than the comp.

Anthropic's careers page says outright that a PhD and prior ML experience are not required. Roughly half their technical staff don't have PhDs. Their literal instruction: if you've done interesting independent research, written a sharp blog post, or made real open-source contributions, put that at the top of your resume, above your degree. Their technical interview runs on Google Colab or Replit with live screen sharing, plus a 90-minute CodeSignal assessment where you build things like in-memory databases, not LeetCode puzzles. So prep by building something real and writing it up, not by grinding Blind 75.

💡

Tip: Anthropic literally tells candidates to put a blog post, open-source contribution, or independent research at the very top of the resume, above the degree line. Treat that instruction as a screening rule, not a suggestion.

This is also where positioning matters more than polish. Anthropic and OpenAI interview differently, and Anthropic wants to see deployed projects and real work, not just degrees. Our Resume Builder gives you ATS feedback and job-fit analysis so you can position your actual work, the blog posts, open-source, and research, exactly the way each lab expects to see it.

OpenAI runs the classic hard-tech gauntlet. Candidates describe needing to be "a coding machine" through a 4-to-6 hour virtual onsite with live medium-to-hard coding, system design, and ML theory questions like KL divergence and classifier accuracy bounds. The whole loop runs about 30 days. So for OpenAI, you do grind the algorithms and brush up your stats.

If you're switching in from physics, math, or plain software engineering with no formal ML background, look at the OpenAI Residency. It's a 6-month program paying $18,333/month, roughly $220K annualized, built specifically for career-changers with strong fundamentals but no ML resume. No PhD required, and residents reportedly convert to full-time.

One more thing that's true at both: cold applications rarely clear the bar. What actually got people in, per write-ups of 2024 and 2025 hires, was either a referral or an unconventional hook, like emailing a paper's authors about a mistake in it. The projects that worked were deployed, documented, and explained: a PyTorch paper reimplementation with a real README, a RAG system with a working UI, a blog post or video walking through it. The ones that failed stopped at a Jupyter notebook with a Titanic dataset and no deployment. Build the deployed version.

⚠️

Note: A notebook with a Titanic or MNIST tutorial and no deployment reads as practice work, not a portfolio. Ship something with a README and a working interface before you apply.

What is the culture really like at OpenAI vs Anthropic?

The clean narrative is "Anthropic is the safety lab, OpenAI sold out." It's more complicated than that, and you should know the messy version before you pick.

The OpenAI-to-Anthropic talent flow is real and documented. Jan Leike, who co-led OpenAI's Superalignment team, resigned in May 2024 saying safety "took a backseat to shiny products," then joined Anthropic. Co-founder John Schulman followed in August 2024. OpenAI quietly removed the word "safely" from its mission statement around October 2025, and dissolved its Mission Alignment team in February 2026.

But the inside view complicates it. A friend of mine actually moved from Anthropic to OpenAI, against the usual direction, because she wanted to be where the paradigm shift was happening and thought post-training was the next frontier. She's also honest that OpenAI churns: the person you collaborate with today might be on another team next month. And what we've seen from candidates who came out of OpenAI is that safety was well-resourced from the inside, pointing to Advanced Voice Mode getting held back four months over deepfake risk, long enough that users thought the demo was fake.

Anthropic has its own tensions. A safety researcher who'd shipped anti-bioterrorism defenses left in February 2026, writing about "constantly facing pressures to set aside what matters most." Even the safety-first lab deals with internal friction.

So here's how I'd actually decide. Pick OpenAI if you want maximum cash now, a near-term liquidity path, and you thrive in a fast-reshuffling, high-agency environment. Pick Anthropic if you don't have a traditional ML pedigree, you'd rather be judged on a blog post and a GitHub repo than a LeetCode score, and you want a culture that's at least structurally built around safety. Then ask both teams the questions that get past the recruiting gloss: How often do people change teams in a year? What happened to the last person in this role? When was the last time a launch got delayed over a concern, and who made that call?

💡

Tip: Bring three diagnostic questions to every final round: how often people change teams in a year, what happened to the last person in the role, and the last time a launch was delayed over a concern and who made that call.

Both labs move fast and their hiring windows close quickly, often before you notice they opened. Simplify's Job Tracker keeps you organized across applications, alerts you to new roles at either lab the moment they drop, and tracks your progress through their different interview loops, so you're never caught refreshing a careers page at midnight.

Frequently asked questions

Is it harder to get hired at OpenAI or Anthropic as a new grad?

Both are tough, but the difficulty sits in different places. OpenAI gates on raw algorithmic and ML-theory horsepower across a long onsite. Anthropic gates on demonstrated, shipped work. If you lack a pedigree but have deployed projects, Anthropic's screen rewards you more. If you grind algorithms well, OpenAI's loop plays to that strength.

Does Anthropic really not require a PhD?

Correct. Anthropic states a PhD and prior ML experience are not required, and about half its technical staff don't hold one. The catch is the bar shifts to proof of work. You're expected to show independent research, meaningful open-source contributions, or a genuinely sharp technical write-up that stands in for formal credentials.

How long does the OpenAI interview process take?

Plan for roughly a month from first contact to decision. The centerpiece is a 4-to-6 hour virtual onsite covering live coding, system design, and applied ML theory. Block real prep time for statistics topics like KL divergence and classifier bounds, since those trip up strong coders who skipped the math refresh.

Which lab offers better equity for a pre-IPO joiner?

It depends on your risk tolerance. OpenAI has shown actual liquidity through a 2025 secondary sale and a rumored near-term IPO, so cashing out is more concrete. Anthropic offers no comparable secondary yet but climbed in valuation faster. One is a clearer exit, the other a steeper bet on growth.

Should I apply cold or find a referral at these labs?

Referrals and unconventional hooks clear the bar far more often than cold applications. A warm intro from someone inside, or a specific, credible reason you reached out, beats dropping a resume into a portal. Build a documented project first, then use it as the reason to start a real conversation with someone on the team.