Learn how to prepare for OpenAI interviews with this in-depth guide.

We break down the OpenAI interview process and the top questions you should expect to answer.

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Verified: This guide was created with insights from candidates who recently interviewed at OpenAI, along with people who conduct its interviews.

About OpenAI

What is OpenAI?

OpenAI is an AI research and deployment company founded in 2015, with the stated mission of ensuring that artificial general intelligence benefits all of humanity. It builds ChatGPT, the GPT model family, and developer tools like Codex, and ChatGPT now reaches roughly 800 million weekly users. By early 2026 the company was valued at around $852 billion, which puts it at the front of the current wave of AI labs.

Where is OpenAI located?

OpenAI is headquartered in San Francisco's Mission Bay neighborhood, with additional offices in New York, Seattle, London, and a handful of other cities. Headcount sits in the several thousands, and here the numbers get fuzzy: company filings and Wikipedia put it near 4,500 in early 2026, while the Financial Times reported a plan to nearly double toward 8,000 by the end of the year, and some trackers already estimate higher. Most roles are hybrid out of San Francisco, typically around three days a week in the office.

Who does OpenAI hire?

OpenAI publishes five core values on its careers page: AGI focus, "intense and scrappy," scale, make something people love, and team spirit. Those words show up in the interviews too. Candidates across roles describe interviewers who care intensely about the mission, value people who tie their work back to it, and look for generalists who can operate with little process.

Most of the engineering organization sits in Applied Engineering (sometimes called Applied AI), which covers ChatGPT and the products around it, separate from the smaller model research teams. The product organization is lean, and product managers operate closer to general managers, owning business outcomes and working directly with legal, finance, and marketing. The design team is tiny relative to the company, so designers tend to own brand, copy, motion, and even front-end code alongside product work.

OpenAI Interview Resources

OpenAI Interview Process

The OpenAI interview process is consistent in shape across most roles, even though the content shifts a lot by team. A typical loop looks like this:

A recruiter or hiring manager screen,
A technical assessment (a coding and system design screen for engineers, or a take-home for data, design, and growth roles),
Final interviews: four to six sessions with four to six people over one or two days.

Leveling and team matching usually happen at the very end, not the start. You can read more first-hand accounts on our OpenAI interview experiences page, and you can see the company's own version on its published interview guide.

How long does the interview process take?

Plan for about three to four weeks from first call to decision, with most candidates landing around a month. OpenAI says you should hear within a week of finishing each stage, and references usually come up only at the offer stage.

Does OpenAI's interview process vary by role?

The skeleton is similar, but the assessment changes a lot. Engineers get live coding and system design. Data scientists, growth leads, and designers get a take-home or a portfolio, then defend it live. Research engineers face the heaviest technical bar, including graduate-level machine learning theory. One quirk that surprises people: there is no interviewer question bank and no formal interviewer training, so engagement and question style swing widely from one interviewer to the next, and many interviewers reuse challenges from their previous companies.

Is there a take-home assignment?

For some roles, yes. Data scientists get a 48-hour data challenge built around an A/B test, growth candidates get a bespoke assignment written by the hiring manager (one recent example asked candidates to design a free trial for ChatGPT Team), and designers prepare a portfolio presentation. Engineering loops skip the take-home in favor of live coding.

How does OpenAI make hiring decisions?

Senior and staff candidates often run the exact same loop, and the level gets decided at the end of the loop instead of up front. An L5 at OpenAI maps roughly to an L6 at Meta or Google. After the loop, strong candidates move into team matching, where a recruiter looks for a team and level that fit. Recruiters can also be upfront about downleveling, and one product manager was told the company usually places people one level below their current title, and sometimes two.

Recruiter and Hiring Manager Screen

The recruiter screen is a real behavioral interview, not a lightweight intro call. Verified loops have included detailed questions about your biggest failure, how you handled conflict, and the full complexity behind a major launch — all in a 30-minute slot. Come with polished stories, not just a background summary.

What is OpenAI's first screen?

The process opens with a 30-minute call, sometimes with a recruiter and sometimes directly with the hiring manager. Many candidates are sourced through cold outreach, occasionally via a contracting recruiter whose email is prefixed with "c/openai" before an in-house recruiter takes over. Expect the standard ground: your background, your interest in OpenAI, your timing, and which teams might fit.

For some roles this screen is heavier than the usual recruiter chat. One product manager's 30-minute screen jumped straight into full behavioral questions about a difficult product launch, a biggest failure, and a moment of team disagreement, which almost never happens that early elsewhere. A growth lead's hiring manager screen split into roughly 15 minutes of behavioral questions and 15 minutes of a mini case, asking how they would design an experiment to onboard new ChatGPT users.

How should I prepare for it?

Be ready to connect your interest to the mission in concrete terms, since "why OpenAI" comes up in nearly every round. Have a sharp version of your strongest project and your biggest failure ready before the first call, because the screen can move into territory that other companies save for the final round.

Technical Assessment

The coding round is structured as five sequential parts — you must pass each part's test cases before the next unlocks. Start coding immediately; explaining your approach first costs time you don't have.

What is OpenAI's technical assessment?

For engineers, the assessment is usually two 60-minute sessions, one coding and one system design, often on CoderPad. The coding side leans practical and favors completeness first: get something working, then optimize, with the interviewer adding edge cases and follow-ups as you go. Interviewers frequently care more about accuracy and clean handling of cases than raw speed. The system design side has a clear obsession with scale, asking how a design holds up at 100 times or 1,000 times the load.

The exact difficulty varies by role, which trips people up. Recruiters sometimes describe the coding challenge as a medium-to-hard algorithmic challenge, but applied engineering candidates often find it closer to a real engineering task, like storing and retrieving state through a small API. Research engineering is a different story, with multi-part challenges that demand graduate-level machine learning and information theory.

What types of rounds are included?

For an OpenAI software engineer interview, the coding session is practical and the system design session is scale-focused, and both run about an hour. For data scientists, the assessment is a 48-hour take-home A/B test challenge submitted as a slide deck, followed by a one-hour review that spends 30 minutes walking through your deck and 30 minutes on live SQL or Python, including reading AI-generated code and fixing what it got wrong. For an OpenAI research engineer interview, expect back-to-back coding screens that test fundamentals you cannot game with a memorized question list.

Is it difficult?

It depends heavily on the role. Applied engineering coding is manageable for anyone comfortable with practical, well-tested code, and Python is a safe default (you can note a different language in your scheduling email). Research roles are where the bar climbs sharply, since the machine learning theory is the part that catches even strong engineers off guard.

Final Interviews

What are OpenAI's final interviews?

The final loop is four to six sessions with four to six people over one or two days, virtual by default with an option to come onsite in San Francisco. The screen rounds are not repeated here, so the full loop adds new dimensions: more technical depth, cross-functional behavioral questions, and a project walkthrough. For engineers, that often means a second coding session, another system design, a standard behavioral round, a cross-functional behavioral round (for example, a time you worked with a legal team), and a project walkthrough that runs close to an hour.

That project walkthrough is the round candidates single out most. Instead of accepting a polished two-minute summary, interviewers dig into what you personally did, why you did it, and who you worked with, in a style several candidates compared directly to Anthropic. OpenAI sometimes asks engineers to prepare a four or five slide presentation about a system they built. If the coding signal is borderline, the company may add one more coding round purely to get more confidence, which is a good sign.

How does it vary by role?

The final loop reshapes itself completely by function. A data scientist faces two case rounds (often experimentation cases where metrics move in mixed directions and you decide whether to recommend a launch), a rapid statistics round, a PM round, and a leadership round. A growth lead's loop is four behavioral rounds, with the questions shared ahead of time, covering go-to-market, data and experimentation, a product partnership with an engineering manager, and marketing. An OpenAI product designer interview runs as an in-person super day: a portfolio presentation to around 10 people, a one-on-one portfolio question round, a design exercise to whiteboard an app built around AI, and an app critique where you break down the design choices in a real product you use.

Is there a non-obvious round to watch for?

Yes. Across functions, interviewers apply a kind of rigor that goes past your history and into the present moment. Even with behavioral questions handed out in advance, the real interview lives in the follow-ups, and they want to see how you would apply a concept to OpenAI's actual business on the spot. Designers describe the same pull toward practicality, with interviewers poking at whether a flow would scale, stay responsive, or hold up given model latency.

OpenAI Interview Questions

These are examples of real interview questions asked at OpenAI. You can find more on our OpenAI interview questions page.

Behavioral

Why do you want to work at OpenAI?
Tell me about a time you disagreed with your manager.
Walk me through your most impactful or memorable project, and what you personally did.
Tell me about your most difficult product launch and your biggest failure.
Describe a time your work had a negative business impact. What happened and why?
Tell me about a time you collaborated with a team that disagreed on a project's approach or goals.
Tell me about a time you had a conflict with someone and how you resolved it.
Tell me about a time you explained a technical limitation to a non-technical stakeholder.

Coding

Implement encode and decode functions for a list of strings, where one is the inverse of the other.
Store and retrieve key-value state to the cloud using provided string-to-byte helper methods, optimizing for space and efficiency.
Implement spreadsheet cell notation: support formulas that reference other cells and update dependent cells, with caching.
Build a chat message API using classes, inheritance, and polymorphism, tracking states like "in a meeting" or "out of office" and a running message counter.

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Practice these in our Software Engineering interview course.

System Design

Design Slack.
Design a video streaming platform that works at global scale, handling different frame rates and large jumps in usage.
Design a system like GitHub and explain how a user interacts with it.
Design a payment processing system (like Stripe).

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Review concepts in our System Design interview course.

Machine Learning

Implement all_gather across multiple nodes, then derive how many rounds you need to reach a target accuracy over a noisy channel, and improve the algorithm by exploiting float values.
Debug a transformer implementation by finding the seeded bugs, then add key-value caching by working out which parts of the model to optimize yourself.
Implement a key-value store serializer and deserializer that can save and restore the state of a system.
Write code for a system that assigns tasks to humans and AI, satisfying a provided list of requirements.

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Prepare with our Machine Learning Engineer interview course.

Product Management

You have invented a memory machine that produces video, image, smell, and sound. Take it to market.
Your model is 10 times more capable and 10 times more expensive. What do you do?
How would you design an experiment or feature to onboard new ChatGPT users?

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Work through frameworks in our Product Management interview course.

Data Science

Given behavioral data from an A/B test, define success metrics and decide whether the launch worked.
Read AI-generated SQL or Python, identify the logic, and correct what the model got wrong.
Explain a p-value and design a factorial experiment.

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Sharpen these in our Data Science interview course.

Tips for Getting Hired at OpenAI

Knowing how to get a job at OpenAI starts with understanding what makes its process different from a standard big tech loop.

Tie everything back to the mission. "Why OpenAI" surfaces in almost every round, and the candidates who do best connect their work and their metrics to the mission instead of treating it as a throwaway question. Read OpenAI's Charter and a few recent posts from its research blog before you interview so you can speak to specifics.

Prepare for relentless follow-ups. The OpenAI hiring process digs for depth, and a clean two-minute story will not survive the questioning. Practice talking about your projects in a way that holds up when someone asks what you did, why, and what you would change. Our mock interview tools help you rehearse that back-and-forth.

Expect practicality over theory. Coding sessions look like real engineering tasks, system design centers on scale and latency, and even behavioral rounds turn into "what would you do here, right now" exercises. Get comfortable applying concepts to OpenAI's own business instead of reciting frameworks.

Match your prep to the role. Applied engineering wants clean, well-tested practical code, while research roles demand graduate-level machine learning and information theory. If you are aiming at research, brush up on the theory early with our Machine Learning Engineer interview course, since that is the part most people underestimate.

Frequently Asked Questions

How long is the OpenAI interview process?

Most candidates move from first call to decision in about three to four weeks. OpenAI aims to give updates within a week of each stage, though some candidates report slower recruiter communication in practice.

Are OpenAI interviews conducted virtually?

Yes, by default. Final loops run virtually, and candidates can choose to come onsite in San Francisco instead. Most roles are hybrid once you join, with several days a week expected in the San Francisco office.

Does OpenAI hire new graduates?

Yes. OpenAI runs a software engineer new grad track that begins with an online assessment of standard coding challenges, then a coding interview, then a final round pairing a second coding session with a behavioral round. The new grad coding bar is high, so finishing every part of the challenge matters.

Does OpenAI offer remote positions?

Remote roles are limited. Most positions are hybrid and based in San Francisco, and several candidates were told there were no remote openings for their team. A small number of roles sit in other offices like New York and London.

Team matching happens after you receive the offer, not before. You complete the full loop without knowing which team you'll land on; you only meet hiring managers once you've accepted. For SWE roles: the final coding round can be OOP and class design rather than classic DSA — verified loops included a layered object-oriented design problem built around a chatbot interface. Prepare for both formats.

Can I get a job at OpenAI without a research background?

Yes. Much of the company sits in applied engineering and product roles that do not require a research pedigree, and OpenAI says it values a wide range of backgrounds. If you are targeting a research engineer position specifically, the machine learning and information theory bar is high and worth dedicated preparation.

Prepare for Your OpenAI Interview

Review recently asked OpenAI interview questions and answers from real candidates.
Practice with our mock interview tools.
Get role-specific OpenAI interview guides.

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Get a Job at OpenAI: Interview Process and Top Questions

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