OpenAI Software Engineer Interview Process: What to Expect
Devana Team
OpenAI publishes its own interview guide, and it is more specific than most. It tells you the stages, how long the final interviews take, what an engineering answer is judged on, and even how it handles AI tools during interviews.
The process, as OpenAI describes it
| Stage | What OpenAI says |
|---|---|
| Application review | Typically about a week for the recruiting team to review your résumé and reply |
| Introductory calls | A conversation with the hiring manager or a recruiter about your experience, motivations and goals |
| Skills-based assessment | Varies by team and may include pair coding, take-home projects or technical tests; possibly more than one |
| Final interviews | Virtual by default, or onsite in San Francisco; typically 4 to 6 hours with 4 to 6 people over 1 to 2 days |
| Decision | Expect to hear within a week of the final interviews; references may be requested |
OpenAI adds that the final interviews focus on your area of expertise and "are designed to stretch you beyond your comfort zone."
What OpenAI assesses
The engineering bar is stated plainly: "For engineering interviews, we generally look for well-designed solutions to the challenge, high-quality code, optimal performance, and good test coverage." Beyond the code, the guide says OpenAI cares about collaboration, effective communication, openness to feedback and alignment with its mission, and that it is "not credential-driven."
AI tools during interviews
The guide says expectations vary: "some formats intentionally allow them, while others are designed to assess your independent problem-solving without AI tools." OpenAI explains what is allowed in your preparation materials and suggests asking your recruiter if unsure. Prepare for both.
What our OpenAI question bank looks like
Devana's question bank holds 38 OpenAI questions, written to reflect how OpenAI interviews: its products, its published values and the round types candidates describe. They are our questions, not leaked ones. How they divide up says a lot about where to spend your preparation.
| Round type | Questions | Examples |
|---|---|---|
| Coding | 13 | Implement byte-pair tokenisation; Stream tokens to a client that may disconnect; Rate limit by tokens rather than requests |
| System design | 8 | Design an inference API for a large model; Design the training data pipeline; Design the evaluation platform |
| Technical deep dives | 11 | Why the model gets slower as the conversation grows; When a model confidently states something false; What reinforcement learning from feedback actually changes |
| Behavioural | 6 | Building something whose consequences you were unsure of; Working on something with no established practice; Moving fast without breaking something important |
23 of the 38 are rated hard. The topics that come up most often are Machine Learning, Scalability, APIs, Systems Design, Security and Strings.
🎯 Practise these out loud
Create a free account, open the question bank and filter by OpenAI. Any question can be practised as a 30-minute voice interview with an AI interviewer that talks, asks follow-ups and runs your code. The free plan includes three interviews a month. Create a free account
Our OpenAI questions are about building with and around large models: tokenisation, streaming responses, rate limits measured in tokens, fitting conversations into a context window, and evaluating whether a model is actually good. Machine learning is the most common topic, ahead of scalability and APIs.
How to prepare
- ✓ Write the tests as part of the answer. Test coverage is named in OpenAI's own engineering bar. Practise finishing a problem with the tests that prove it works.
- ✓ Practise building, not just solving. Pair coding and take-home formats reward working, well-structured code. Build small end-to-end things against a deadline.
- ✓ Practise with and without AI help. Since some formats allow tools and others forbid them, make sure you can solve problems unaided as well as direct an assistant well.
- ✓ Do the reading OpenAI recommends. The guide points to the OpenAI Charter, its research publications and blog, and for technical reading the Deep Learning book and Spinning Up in Deep RL. Read the recent work of the team you are interviewing for.
- ✓ Know the model-serving basics. Why latency grows with conversation length, how to stream safely and how to rate limit fairly come up naturally. Machine learning engineer interview prep covers the ML side.
Frequently asked questions
How long is the OpenAI interview process?
OpenAI's guide describes about a week for résumé review, a week between stages, final interviews of typically 4 to 6 hours over 1 to 2 days, and a decision within a week of those.
Can I use AI tools in an OpenAI interview?
It depends on the format. Some intentionally allow them and some do not; your preparation materials will say which, and your recruiter can confirm.
Do I need a degree to work at OpenAI?
OpenAI's guide says it is not credential-driven and is interested in high-potential people who ramp up quickly in a new domain, as well as established experts.
