OpenAI vs Anthropic Software Engineer Interview: Two Different Tests

You've decided you want to work at an AI lab. Great. You open LeetCode, grind for eight weeks, and feel ready. Then you discover that one of these companies wants you to write a distributed webhook system over 48 hours, and the other wants to have a 45-minute conversation about your personal ethics. They're both hiring software engineers. They are not running the same process.

If you're targeting OpenAI, Anthropic, or both, generic Big Tech prep will leave you exposed. Here's how the two processes actually differ, where candidates get filtered, and what it takes to pass each.

The Process at a Glance

Stage	OpenAI	Anthropic
Recruiter screen	30-45 min, mission framing	30 min, safety framing
Coding assessment	60-min live progressive gate	90-min CodeSignal OA (4 levels)
Take-home	48-hour engineering project	None (CodeSignal replaces it)
Hiring manager screen	Not always included	45-60 min, engineering judgment
Onsite	4-6 hrs, 4-6 rounds	~4-5 hrs, 4-5 rounds
Values round	Half of behavioral round	Dedicated 45-min standalone round
System design flavor	AI-scale infra, distributed systems	LLM infrastructure, ML pipelines
Timeline	4-8 weeks	4 weeks to 3+ months

The Recruiter Screen Already Filters You

Both calls open with "why this company specifically?" and both interviewers are listening for something real. Not your LinkedIn headline. Not "I'm passionate about AI." Something that suggests you've actually thought about what these organizations are doing and why it matters.

At OpenAI, the frame is AGI and what role the company's products play in getting there. At Anthropic, it's AI safety. You don't need to deliver a TED talk, but you do need to have formed an opinion before the call.

Generic "I love AI" answers fail at both labs. Candidates who treat recruiter screens as formalities get cut before the technical work starts.

Coding Goes in Very Different Directions

OpenAI's technical screen is a 60-minute live session built around a gate structure. A single problem starts simple, then escalates. Most candidates need to pass at least two gates to advance. The problems simulate real systems: a time-based key-value store, a resumable iterator for large datasets, a sliding-window rate limiter.

Think of it as a video game boss fight that keeps getting harder. You clear level one, the boss grows two extra heads. The emphasis is clean, working code at each level rather than racing to the end.

Anthropic uses a 90-minute CodeSignal OA you complete on your own time. Same four-level escalating format, but asynchronous and scored against a rubric. Candidates report needing roughly 520+ points to advance. The problem types overlap: build an in-memory key-value database starting with GET/SET/DELETE, then add filtered scans, TTL expiry, and eventually snapshot or compression logic.

One thing worth knowing: Anthropic uses LLMs to detect code that pattern-matches against visible edge cases without genuinely solving the problem. Write code that's actually correct, not code that games the rubric.

Joey from Friends facepalm: Getting the algorithm to run in O(1) at the last minute. Interviewer: Can we do better?

Getting to O(1) felt like winning. It was not winning.

OpenAI Has a 48-Hour Take-Home. Anthropic Doesn't.

This is the biggest structural split between the two processes.

After the technical screen, OpenAI sends a take-home project with a 48-hour window. That's approximately the time it takes to build something reasonable, second-guess every design decision you made, rebuild part of it, and wonder if "reliable" means what you think it means. The task is a real engineering problem: a distributed webhook delivery system with retry logic, exponential backoff, and dead-letter queues. You're graded on reliability, code quality, and testing coverage, not feature count.

The follow-up interview reviews your submission line by line. Be ready to defend every design choice. Why that retry strategy? Why not a circuit breaker? The take-home isn't just an assignment. It's the script for the next conversation.

Anthropic skips the take-home entirely. The CodeSignal OA does that filtering work, and the onsite moves straight to live rounds. If you do your best work under time pressure and hate asynchronous projects, Anthropic's structure fits better. If you'd rather have time to think carefully and write tests, OpenAI's 48-hour window is genuinely an opportunity.

System Design Is Domain-Specific at Both

Neither company wants to hear about designing a generic social media feed. Both expect you to have internalized what they actually build.

At OpenAI, system design problems are grounded in the infrastructure behind frontier models: distributed training pipelines, inference serving at scale, vector store architecture, job queuing for long-running model evaluations. You don't need a lab background, but you need to understand the engineering constraints that come with large-scale model deployment.

Anthropic's system design questions are explicitly LLM-centric. Expect: design a scalable inference serving system for a large language model, design a document retrieval pipeline with embedding updates, design a monitoring system for model behavior at scale. Concurrency and multithreading come up across multiple rounds, not just system design. If your distributed systems fundamentals are rusty, that's the first gap to close.

Anthropic's Values Round Is the Round That Catches People Off Guard

Here is where the two companies diverge most sharply, and where a lot of technically strong candidates get eliminated.

Anthropic runs a dedicated 45-minute culture interview conducted by a non-technical interviewer. Every candidate at every level goes through it: engineers, PMs, researchers, sales. The questions are personal and probing. "Describe a time you pushed back on something you thought was wrong." "How would you handle a project that felt ethically questionable?" "What does responsible AI development mean to you in practice?"

If you've spent three months grinding Dijkstra's and then get knocked out in a philosophy round, that's a particular kind of pain.

Gus Fring from Breaking Bad: You put your paladin into Morton's fork situations to force them to be edgy oathbreakers. I put my paladin into Morton's fork situations to force them to address the inherent greyness of morality. We are not the same.

Anthropic's values round, basically.

Anthropic recruiters have said the values round is where most candidates fail. Not because they give wrong answers, but because they give rehearsed ones. The interviewers probe for genuine conviction about AI safety, not a recitation of talking points. They send reading material before the round: Anthropic's Core Views on AI Safety, the Responsible Scaling Policy, recent research. Candidates who can push back thoughtfully on Anthropic's own positions tend to score better than those who just agree.

This round has no equivalent at OpenAI. OpenAI's behavioral interview is roughly half ownership-and-impact stories and half questions about the company's mission. It's substantive, but it's one component of a longer behavioral round, not a standalone gate.

Mission Alignment Is Framed Differently at Each Lab

Both companies care about mission. The framing matters for how you should prep.

OpenAI's emphasis has shifted. The company went from a nonprofit research lab to the organization behind ChatGPT, the fastest product to reach 100 million users in history. The behavioral interview probes for ownership, speed, and comfort moving fast with imperfect information. Mission alignment is tested, but so is your bias toward action.

Anthropic was founded explicitly over disagreements about pace and safety at OpenAI. The culture has stayed research-oriented and safety-first, with more emphasis on intellectual humility and long-term thinking.

Arriving at Anthropic with "I want to ship fast" energy, or at OpenAI with "I won't do anything until it's fully safe" energy, will read as a mismatch. Neither instinct is wrong. They're just different organizations selecting for different things.

Who Each Process Is Actually Designed to Find

Read the structure and it becomes clear.

OpenAI's process rewards candidates who write clean production code quickly, communicate design decisions under time pressure, and have thought seriously about building at scale without losing velocity. The 48-hour take-home rewards engineers who scope well and deliver a reliable system.

Anthropic's process rewards candidates who build incrementally, think carefully before acting, and have actually internalized why safety matters. The values round exists because Anthropic treats mission alignment as a hard requirement, not a cultural add-on.

If you want to work on the world's most-used AI products, OpenAI. If you want to work somewhere "is this safe?" is the first question on every project, Anthropic.

What to Actually Practice

For OpenAI:

Practice progressive problems: solve a system, then extend it under new constraints. LeetCode medium systems problems are a good starting point.
Treat the take-home like production code: structure, tests, documentation. A reliable simple system beats a clever broken one.
Prep ownership stories. "Tell me about a time you acted as an owner" is a near-certain question. Have a real example ready.
Read broadly on distributed systems, inference serving, and model training pipelines. You need to reason about them, not have built them.

For Anthropic:

Drill the CodeSignal four-level format. Practice building systems that grow incrementally: start with the simple version, then layer in constraints.
Concurrency is a recurring theme. Study locks, thread safety, and async patterns.
For the values round: read Anthropic's Responsible Scaling Policy and form actual opinions. The interviewers will probe your reasoning, not just your conclusions.
System design prep should focus on LLM-specific infrastructure: embedding pipelines, inference serving, model monitoring.

For either company, the onsite involves a lot of spoken reasoning under pressure. Voice practice matters more than most people expect. Getting reps on SpaceComplexity before your onsite is worth the time.

For deeper dives on each process individually, see the OpenAI software engineer interview guide and the Anthropic software engineer interview guide. If you're preparing for multiple AI labs at once, the Microsoft vs Google interview comparison covers how to manage parallel prep for companies with different signals.