OpenAI interview questions and process: what to expect and how to prepare

In this article

1. Before you start
2. Why interviewers ask this
3. The OpenAI interview process from first principles
4. Worked example
5. Trade-offs and edge cases
6. How to answer it in an interview
7. Common mistakes
8. Follow-up questions
9. Sources

Before you start

Everything else, including the vocabulary of the process, is explained below.

Why interviewers ask this

Candidates search for OpenAI's questions because the stakes of one loop are high and the company's output is public, so the loop feels guessable. The consequence of guessing is wasted preparation: a candidate who drills one remembered question can still stall on a follow-up that changes a constraint.

What the loop separates is depth under change. A weak preparation memorises answers. A strong one knows the mechanism well enough to re-derive it when the interviewer alters the problem. OpenAI says in its guide that it aims for a consistent process for all candidates and is not credential-driven, which points the same way: the interviews look for what you can do and explain, not for a list of employers or degrees.

The OpenAI interview process from first principles

In plain words

Final interviews

Final interviews are the last stage of a hiring process, a set of back-to-back conversations with several interviewers that together produce the decision.

Forward-deployed engineer

A forward-deployed engineer is an engineer who works directly with customers to deploy a company's technology in their real systems, so the role mixes engineering with customer problem-solving.

Open-weight model

An open-weight model is a language model whose trained parameters are published so that others can run and adapt it on their own hardware.

Think of the process as a funnel with five gates. The guide gives a one-week wait at four of them. Each gate checks one thing cheaply before the next one spends more of everyone's time. The analogy breaks at the end. A funnel only removes people, while the last stage also tells you about the team, so you are interviewing them too.

In short, the process runs from a résumé review to a conversation, then a skills-based assessment, then a multi-interviewer final round, then a decision. The published guide states the stages, the waits between them and the final round's size. It gives no list of questions.

1 · Application review2 · First conversation3 · Skills-based assessment4 · Final interviews5 · Decision, referencesReplay

Step-by-step description

1. The recruiting team reviews your application, typically in about a week.
2. A hiring manager or recruiter asks about your work, motivations and goals.
3. Pair coding, take-home or technical tests; formats vary by team.
4. Typically 4 to 6 hours, 4 to 6 people, 1 to 2 days.
5. Expect an answer within about a week; references may be requested.

• The recruiting team typically takes about a week to review an application.
• The first conversation is with a hiring manager or recruiter about work, motivations and goals.
• The skills-based assessment may be pair coding, a take-home project or a technical test, and you hear within a week.
• Final interviews typically take 4 to 6 hours with 4 to 6 people over 1 to 2 days.
• Candidates expect to hear back within about a week of the final interviews.

Step 1: Application review

The recruiting team reviews your application and resume. OpenAI's guide says this typically takes about a week, after which you hear back by email. The input is your application, the change is a yes or no on moving forward, and the output is a first contact. Make the resume specific: name the system, the scale and your own part in it. What you now know: the first gate checks fit with the role, not the interview.

Step 2: The first conversation

If there is a fit, you are scheduled for a conversation with a hiring manager or recruiter about your work, your motivations and your goals. This conversation is where "why this role" is tested, so prepare two minutes on a project you can defend in depth, and one clear reason for choosing this company and this job. The output is the decision to invest in assessments. The tell me about yourself page shows how to shape the first two minutes.

Step 3: Skills-based assessment

After the first conversation, OpenAI's guide describes a skills-based assessment: "Formats vary by team and may include: pair coding interviews, take-home projects, technical tests." It adds that you may be asked to complete "more than one assessment depending on the role", that the recruiting team "will provide prep", and that you hear "within a week" whether you advanced (OpenAI interview guide, read October 2026). The input is a task; the output is evidence of how you reason. Ask your recruiter which format applies and whether AI tools are allowed, because the guide says expectations vary by interview. Prepare with the ML coding hub and the ML system design hub.

Step 4: The final interviews

OpenAI's guide says final interviews typically run 4 to 6 hours with 4 to 6 people over 1 to 2 days, virtually by default with an option to interview onsite in San Francisco. The guide says interviews are focused on your area of expertise and designed to stretch you. For engineering interviews it names four things it generally looks for: "well-designed solutions to the challenge, high-quality code, optimal performance, and good test coverage", and it evaluates communication and collaboration (OpenAI interview guide, read October 2026).

Those are the criteria to rehearse against. Do the arithmetic in the worked example below to plan stamina: 4 to 6 hours across 4 to 6 people is roughly 40 to 90 minutes per interviewer. The guide does not publish individual slots, so treat any list of "the OpenAI questions" as a candidate report, not as policy.

Step 5: Decision and references

The guide says candidates should expect to hear back within about a week of the final interviews, and that you may be asked for references at this stage. The practical step is to line up two people who have worked closely with you and brief them. The guide states four one-week waits: résumé review, notice after the introductory call, the result after the assessment, and the decision after the final interviews. It gives no total, and it does not count scheduling time, so ask your recruiter for the current timeline, and prepare questions for the team from the questions to ask the interviewer page.

Worked example

What a final round means in minutes.

A final round of 4 to 6 hours with 4 to 6 people means that, if the time is split evenly, each interviewer gets between 40 and 90 minutes of your time, and about 60 minutes at the midpoint ¹. The figures are derived from the ranges in the guide; individual slots are not published.

Text description of the diagram

• Lowest case: 4 hours with 6 people is 40 minutes per interviewer.
• Midpoint: 5 hours with 5 people is 60 minutes per interviewer.
• Highest case: 6 hours with 4 people is 90 minutes per interviewer.

The practical reading is stamina: two days of two to three hours is a different load from one day of six, so plan food, breaks and a quiet room for a virtual loop.

Trade-offs and edge cases

The process differs by role. A research role, an engineering role and a forward-deployed role weight the interviews differently, and the guide's single description cannot show that. Ask the recruiter for the exact rounds and what each one assesses.

Employer processes change. This page reflects the guide as read in October 2026, so re-check it before your loop and treat anything older than a few months as a prompt to ask.

Public work is the best open study material, because the company's papers show how it reasons. The Generative Pre-trained Transformer (GPT) 3 paper reports a 175-billion-parameter model applied to tasks "without any gradient updates or fine-tuning" (Brown et al., 2020), which showed few-shot prompting working at scale (see the prompting hub). The InstructGPT paper reports that outputs from a 1.3-billion-parameter model trained with human feedback were preferred over those of the 175-billion-parameter GPT-3 (Ouyang et al., 2022).

The Codex paper reports 28.8% of problems solved on its HumanEval benchmark, rising to 70.2% with 100 samples per problem (Chen et al., 2021). Reading a paper does not mean you will be asked about it; it means you can discuss why a technique works, which is the skill an interviewer is probing. For serving topics, see large language model (LLM) inference and the key-value (KV) cache explainer.

Anthropic runs a different loop; see the Anthropic interview process page rather than assuming the two match.

How to answer it in an interview

A common question in AI-lab loops is how you would compare using OpenAI's models with an open-weight alternative. The answer shows judgement, not allegiance. First define what good means and build a small test set from real inputs, then measure quality, latency and cost per request for both options, then let constraints on data, control and operating work decide close calls.

Text description of the diagram

• Define what good means and build a small test set from real inputs first.
• Measure quality, latency and cost per request for both options.
• Constraints on data, control and operating work decide close calls.

Spoken structure: say the test first, the measurement second, the constraints third and the exit plan last. The answer is 87 words, sayable in under a minute. The deeper methods for building such a test are in the prompting hub, and OpenAI's own documentation recommends building evals and pinning production applications to specific model snapshots (OpenAI documentation, read October 2026). An eval is a repeatable test that scores a model's outputs on your task.

Common mistakes

(wrong) "I memorised the questions candidates posted about this company." The right move is to learn the mechanisms behind the topics, because the follow-up changes a constraint. (right) "I know how this works well enough to redo it when you change the requirement."

(wrong) "I will answer every question about the company's products with praise." The right move is balanced judgement. (right) "Here are the strengths I would use it for and the limits I would plan around."

(wrong) "The guide says the process is consistent, so every loop is the same." Loops vary by role and team. (right) "I asked the recruiter which rounds apply to this role."

(wrong) Treating a third-party blog's round list as official. (right) Treat the company's own guide as the source and mark everything else as candidate-reported.

Follow-up questions

How do I prepare for an OpenAI interview?

Learn the stages from the guide, choose one project you can defend deeply, practise ML coding and design topics, and read the company's public papers for the reasoning behind its techniques. Confirm the exact rounds with your recruiter.

How do I get a job at OpenAI?

Apply with a specific, evidence-led resume, expect an application review of about a week per the guide, and prepare for a first conversation about your work, motivations and goals. The guide says the process is not credential-driven.

Why do you want to be a forward-deployed engineer, not just work at OpenAI?

Name what the role adds: customer-facing deployment work in messy real systems. Give one example where you shipped technology into a customer's environment and what broke, because the second half of the question is about production messiness.

What are OpenAI coding interview questions?

The guide publishes no question list. It says the skills-based assessment may include pair coding interviews, take-home projects or technical tests, and that engineering interviews look for well-designed solutions, high-quality code, optimal performance and good test coverage. Any list of "OpenAI questions" elsewhere is a candidate report and may be outdated; practise on the ML coding hub and the wider AI engineer question list.

What are OpenAI system design interview questions?

The guide publishes no design questions either. It says final interviews focus on your area of expertise and are designed to stretch you, so practise the method on the ML system design hub and confirm with your recruiter which rounds apply.

Sources

1. OpenAI interview guide — OpenAI, 24 Oct 2026
2. Language Models are Few-Shot Learners (arXiv 2005.14165 v4) — arXiv (Brown et al.), 22 Jul 2020
3. Training language models to follow instructions with human feedback (arXiv 2203.02155) — arXiv (Ouyang et al.), 4 Mar 2022
4. Evaluating Large Language Models Trained on Code (arXiv 2107.03374 v2) — arXiv (Chen et al.), 14 Jul 2021
5. Prompt engineering guide — OpenAI documentation, 24 Oct 2026