OpenAI Interview Guide: Process, Questions, and How to Prepare

The OpenAI engineering interview runs in five stages. It starts with a recruiter or hiring manager call. After that come a practical coding screen and a system design interview for many roles.

The last two stages are a final onsite loop of four to six interviews, then team matching. Candidates report about four to eight weeks once interviews begin. Slow scheduling can make the whole process last more than three months in busy periods.

This guide covers each stage, what interviewers grade, and the questions candidates report. It also covers how the loop changes by role, and how to prepare.

If you are interviewing for a backend role, our OpenAI backend engineer interview guide covers the role-specific rounds and reported questions. There are matching guides for the frontend and full stack loops, which differ from the backend one in their coding and design rounds.

The Interview Process

Not every candidate sees every stage. The early system design round depends on the role and the team. For a longer stage-by-stage account, read What Is the OpenAI Interview Process Like? (Round by Round).

One thing is consistent across stages. Interviewers prefer solid engineering and clear explanation over clever tricks.

The Recruiter and Hiring Manager Screen

The first call is short and mostly about fit. It usually runs about 30 minutes. The recruiter asks about your background, the work you want next, and why this company.

The hiring manager version goes deeper into your recent projects. Expect questions about what you built, what you decided, and what you would change.

"Why OpenAI?" is asked more than once. Candidates report it in the recruiter screen, again with the hiring manager, and often once more in the final loop. A generic answer about loving AI is the most common mistake here.

Name a specific product or research direction, and connect it to your own work. Our answer on How to Answer: "Why Do You Want to Work at OpenAI?" gives a three-part structure.

The Coding Round

The coding screen is practical, not puzzle-based. It runs 45 to 60 minutes. You write code that runs, in your own editor or a shared one.

The task is usually a small piece of real software. You may parse an input format, build a small client for an API, or implement a rule engine. Then the interviewer changes the requirements and asks you to extend your code.

Three things are graded. Does the code work? Is it readable and tested?

The third is communication. Can you explain your choices while you type?

Get a correct version working first. Then improve it. A half-finished optimal solution scores worse than a complete simple one.

What interviewers grade here, roughly in order.

• Correctness before speed. A simple working answer beats a fast broken one.
• Test cases you write without being asked for them.
• Depth in your language, not only its syntax.
• Clean structure when the requirements change mid-round.

Candidates also report less help than they expected. The round is built for you to make progress on your own. Say what you are trying before you ask for a hint.

Some teams add a take-home project instead of a second live round. Candidates report a paid work trial with a short deadline, often about two days.

It is graded like production code. Interviewers look at structure, tests, and a short written note on your trade-offs.

Data structure and algorithm questions still appear, usually at medium to hard difficulty. Pattern practice is the fastest way to prepare for those. Grokking the Coding Interview groups the common questions into a small set of repeatable patterns.

The System Design Round

System design matters more here than at many companies. Depending on the role, you may face it twice. It can appear as an early screen and again, in more depth, inside the final loop.

The questions mix classic infrastructure with product design. Reported examples include designing the OpenAI Playground and an enterprise search system built on a large language model. Others include a notifications service, a chat product, and a distributed job scheduler.

For product questions, interviewers expect more than a diagram. They want a clear API contract, a storage design, and an opinion about the user-facing part.

Then come the follow-up questions. Why this queue? What happens at ten times the traffic?

Failure questions are common too. What fails first, and what does the user see when it does?

Scoping is graded too. You cannot design everything in 45 minutes. Say what you will cover first, and say why.

What interviewers grade here.

• How the design behaves at 100 and 1,000 times the traffic.
• What happens to a request in flight when part of the system fails.
• Depth on distributed mechanics when they push on one part.
• Which trade-offs you raise first, and why those.

Name a property rather than a product when you are not sure. "A durable log" is a safer answer than "Kafka", which invites a full comparison against the alternatives.

What to Expect in the OpenAI System Design Interview lists more example questions and a minute-by-minute plan for the hour. If you are starting from the basics, How to prepare for a OpenAI system design interview? covers the core parts you need to know first.

The Behavioral Round

Behavioral questions appear in several rounds, not only one. You will hear them in the recruiter screen, in dedicated team fit conversations, and inside technical rounds through the judgment you show.

Five themes appear again and again.

• Motivation for the work.
• Releasing software quickly with incomplete information.
• Ownership beyond your job description.
• Disagreement with teammates.
• Learning from a failure.

A typical question: "Tell me about a time you shipped something important under a tight deadline. What did you cut, and how did you decide?" Another one: "Tell me about a significant mistake or failed project. What did you learn?"

Answer with specifics. Name the numbers, the timeframes, and what you personally changed afterward. Our answer on Top OpenAI Behavioral Interview Questions (and How to Answer Them) has a worked example for each theme.

What interviewers grade here.

• A view of your own on AI, with a reason behind it.
• Concrete handling of conflict, including what you actually did.
• Working with functions outside engineering, like legal or product.

The Project Walkthrough

Some loops include a presentation of your past work. Candidates report a session of about 45 to 60 minutes. You prepare a short slide deck about one project you led. Slides are expected, not optional.

This is not a polished summary. Interviewers ask what you personally did, why you chose that approach, and what the other options were. Be ready to defend a decision you made long ago.

Scale matters in the answer. Interviewers look for real numbers, or numbers you can defend. Bring request volume, data size, latency, and team size.

Reported OpenAI Interview Questions

These are questions candidates report from real loops, grouped by round. Treat them as practice material rather than a list to memorize. Interviewers change the follow-up questions, and the follow-ups carry most of the score.

Coding questions

• Implement spreadsheet cell notation, where a formula refers to other cells and an update spreads to every dependent cell.
• Build an iterator, then extend it to two dimensions, then to an asynchronous version.
• Implement encode and decode for a list of strings.
• Build a chat interface with classes, then add commands like status tracking.
• Refactor deeply nested code to support a new requirement, with the existing tests still passing.
• Implement a GPU credit management system that tracks usage across companies with very different usage patterns.
• Design a chess game.
• Implement an LRU or LFU cache.
• Build a store where every value carries a timestamp, so you can read the value as of any past time.

Notice what these share. Each one starts small, works, and then grows through new requirements. Almost none of them is a single-answer puzzle.

System design questions

• Design a job scheduler, with follow-ups on distributed execution and fault tolerance.
• Design Slack, with follow-ups on what changes at 100 and 1,000 times the traffic.
• Design a payment processing system, with follow-ups on retries, idempotency, and recovery after a failure.
• Design the OpenAI Playground, from the interface down to the architecture.
• Design a streaming platform that keeps working as users and data grow.
• Design an enterprise search system built on a large language model.
• Sync key and value state to and from the cloud.

Idempotency means an operation gives the same result whether it runs once or five times. It appears in every payment and scheduler question, because retries cannot be avoided.

For the full bank with worked approaches, read OpenAI System Design Interview Questions.

Machine learning and research questions

These appear for research and applied AI roles, not for every engineering loop.

• Explain why layer normalization improves training stability.
• How does a transformer learn long-range dependencies, and what makes that possible?
• Find and fix the bugs in a transformer implementation of about 300 lines, then add KV caching.
• Implement all_gather across a communication channel that drops messages.
• Derive the expected number of iterations of a probabilistic function.

Two terms there are worth defining. The operation all_gather leaves every machine holding the data from every other machine. KV caching stores past attention keys and values so the model does not compute them again on the next token.

Behavioral questions

• What is your view on where AI is headed, and how we will use it?
• Where do you see AI going wrong, or being misused?
• What draws you to the mission here, and how does it connect to your own work?
• Why this lab, and not another one?
• Tell me about a conflict with a coworker or a stakeholder.
• Tell me about a time you worked with a legal team, or another function outside engineering.
• Tell me about a time you disagreed with your manager.

Interviewers grade the AI questions seriously. Candidates report them in the recruiter screen and again in the loop. A view of your own, with a reason behind it, scores better than enthusiasm.

Project presentation questions

• Did you work on this end to end?
• How long did the project run?
• What were the technical challenges, and how did you handle them?
• Who did you work with, and what was that like?
• What did you learn, and what would you do differently now?

How the Loop Differs by Role

The engineering loop and the research loop are not the same. The shared parts are coding, the project round, and behavioral. The difference is what each role adds.

That column counts individual interviews, not the five stages in the table above.

Research engineering adds two rounds worth preparing separately. One combines coding with machine learning statistics, and candidates report information theory in it. The other is a debugging round, where you are given a model implementation with bugs planted in it and asked to find them.

Backend and infrastructure roles keep the standard loop but ask more about distributed systems. Our OpenAI backend engineer interview guide covers those rounds and the questions reported for them.

Levels reported by candidates run from L2 to L6. Senior and staff candidates go through the same loop. The level is set by how you perform, not by the title you applied for.

After the Interview

The reported end-to-end timeline is about four to eight weeks once interviews begin. Some candidates report a decision in around a month. Others wait longer when scheduling is slow.

Recruiters aim to update candidates within about a week of each stage. That target is not always met, so a quiet week does not mean a rejection.

Rejections usually come as a short email from the recruiter. Detailed feedback is rare, and asking for it seldom produces much. Many companies limit feedback for legal reasons, and this is typical rather than unusual.

If you hear nothing for more than a week after a stage, send one short message to your recruiter. Ask about status and next steps.

One follow-up is fine. Repeated messages do not help.

A pass is not the last step. Team matching calls decide which group you join and at which level.

Treat those calls as part of the evaluation. If you want to know how selective the process is before you apply, read Is OpenAI interview hard?.

How to Prepare

• Practice coding patterns, not question counts. Medium and hard questions repeat a small number of patterns. Learn the patterns and you cover far more questions per hour of study.
• Write code that runs. Practice in a real editor with tests. Solve the problem end to end, then improve it. This matches the format of the screen.
• Rehearse system design out loud, with a timer. Give yourself 45 minutes. Cover requirements, the API, the core path, one deep section, and failure cases. Our course Grokking the System Design Interview covers the parts and trade-offs these rounds ask about.
• Prepare five behavioral stories. One each for speed, ownership, conflict, failure, and scale. Write them down. Keep each one under two minutes.
• Write a real answer to "Why OpenAI?" Name a specific product, research direction, or release. Connect it to something you have actually built.
• Study guides for other companies too. Most people interview in several places at once. Our tech company interview guides cover the process at each one.

Frequently Asked Questions

How many interview rounds does OpenAI have? The process has five stages. The final onsite loop alone holds four to six interviews. Counted as separate conversations, that is often seven or more people.

How long does the OpenAI interview process take? Candidates report about four to eight weeks once interviews begin. Some finish in around a month. Busy hiring periods and scheduling delays can extend it past three months.

Does OpenAI ask LeetCode style questions? Sometimes, at medium to hard difficulty. The more common format is practical coding, where you build something small that runs and then extend it. Prepare for both.

Do I need a PhD to work at OpenAI? Not for engineering roles. Research roles often expect a PhD or equivalent research experience. Engineering hiring focuses on demonstrated skill, shipped work, and system knowledge.

What should I do after a rejection? Wait before you reapply, and change something real in the meantime. Detailed feedback is rare, so use the rounds that felt weakest as your study plan. Many people pass on a later attempt.

Is the system design round harder than the coding round? It counts for more in senior roles, and it can appear twice. The questions are open-ended, so preparation matters more. Scoping and follow-up answers matter more than the first diagram.

What level will I be hired at? Reported levels run from L2 to L6. Senior and staff candidates go through the same loop, and the level follows your performance. Team matching calls confirm it near the end.

Do research and engineering roles run the same interview? No. Research engineering adds a machine learning statistics round and a model debugging round. The coding, project, and behavioral rounds are similar in both loops.

Are OpenAI interviewers helpful during the round? Candidates report less help than at other large companies. The rounds are built for you to make progress alone. Say what you are trying before you ask for a hint.

All OpenAI Interview Questions and Answers

The answers linked in the sections above cover the process, the system design round, and the behavioral round. These cover the company and the role itself.

Role-specific guides

• OpenAI Backend Engineer Interview: Rounds, Questions, and How to Prepare
• OpenAI Frontend Engineer Interview: Rounds, Questions, and How to Prepare
• OpenAI Full Stack Engineer Interview: Rounds, Questions, and How to Prepare

Company and role questions

• How many hours do you work at OpenAI?
• How hard is it to join OpenAI?
• What degree do you need to work at OpenAI?
• OpenAI Applied AI interview focus

Wondering how long the wait is at each stage? Read How Long Does It Take to Hear Back After an OpenAI Interview?.