OpenAI

Official guidance

OpenAI develops frontier AI models and ships them through ChatGPT, Codex and an API platform. Engineering work spans research engineering and model training, large-scale training and inference infrastructure, applied AI and product engineering, safety work, and conventional backend, frontend and platform engineering.

Relevant roles: General software engineering, Backend engineering, Applied AI engineering, ML engineering, Research-oriented ML implementation, Training / inference infrastructure, Research engineering. Domains: Algorithms, Software engineering, ML & applied AI, AI infrastructure, AI research, Interview skills.

What the sources establish

OpenAI’s official interview guide (read from a 2026-08-22 archive capture because the live page blocked automated access) documents the stages: résumé review, introductory calls, team-specific skills assessments (pair coding, take-home projects or technical tests), and final interviews typically totaling 4 to 6 hours with 4 to 6 people over 1 to 2 days. It states engineering criteria (well-designed solutions, code quality, performance, test coverage) and that AI-tool rules vary by interview and are explained in your preparation materials. Not established: technical topics per role, languages, which teams use which formats, and any change to the live page after the capture.

OpenAI interview guide
Official
OpenAI. Undated page; read 2026-10-05 from an Internet Archive capture of this URL taken 2026-08-22, because the live page returned HTTP 403 to automated access. Read in full for this page.
Scope: All OpenAI roles; the page says formats vary by team and role
Documented by this source
- Application and résumé review typically takes about a week; introductory calls with the hiring manager or a recruiter cover experience, motivations and goals
- Skills-based assessment formats vary by team and may include pair-coding interviews, take-home projects or technical tests; some roles require more than one assessment, and the recruiting team provides preparation
- Final interviews are virtual by default, with an optional onsite in San Francisco, and typically total 4 to 6 hours with 4 to 6 people over 1 to 2 days, focused on your area of expertise
- Engineering interviews look for well-designed solutions, high-quality code, optimal performance and good test coverage; communication and collaboration are also evaluated
- A decision typically follows within a week of the final interviews, and references may be requested
- Suggested reading: the OpenAI Charter, research publications and blog posts, plus the Deep Learning book and Spinning Up in Deep RL for technical preparation
- Describes the process as consistent and not credential-driven, valuing collaboration, communication, openness to feedback and mission alignment
Outside or AI tools, according to this source: Expectations for AI and other tools vary by interview: some formats intentionally allow them, while others assess problem solving without AI. Your interview preparation materials say what is allowed; ask the recruiter if unsure. Follow the instructions in your own assessment invitation.
- Read via an Internet Archive capture dated 2026-08-22 because the live page blocked automated access; the live page may have changed since.
- The AI-tools paragraph does not appear in the 2026-07-09 capture, so it was added recently; check the live page and your own instructions.
- Generic across roles: it lists no technical topics or languages and does not say which teams use which assessment format.
Open source(opens in a new tab)Not checkable automatically, 5 Oct 2026
Check note: The site refused or challenged the automated request (HTTP 403). This is common for bot protection and does not mean the link is broken.

Our preparation recommendation

This is our suggestion based on the role and the sources above. It is not a statement by OpenAI.

Our recommendation, not OpenAI guidance: because assessments vary by team and finals focus on your area of expertise, prepare practical, production-quality coding (clear structure, tests, attention to performance) for pair coding or take-homes, and deepen the ML topics that match your role. The guide’s suggested reading points research-leaning candidates toward deep-learning fundamentals and reinforcement learning. Read the AI-tool rules in each assessment’s preparation materials rather than assuming a single policy.

Coding fluency and standard-library costsNot started
Pair coding and take-homes reward fast, idiomatic code in your main language.
Complexity analysisNot started
Optimal performance is a stated engineering criterion.
Testing strategyNot started
Good test coverage is a stated engineering criterion.
Data modeling, ownership and dependency boundariesNot started
Well-designed solutions are a stated engineering criterion; clear structure and boundaries are one way to show design quality.
Neural networks, backpropagation and numerical stabilityNot started
The guide recommends the Deep Learning book for technical preparation.
Reinforcement learning foundations (optional)Not started
The guide recommends Spinning Up in Deep RL; most relevant for research-leaning roles.
Transformers, attention and language-model objectivesNot started
For ML and research roles, since finals focus on your area of expertise in model-centric work.
Reading papers and locating the evidence for claimsNot started
The guide suggests reading OpenAI research publications and blog posts before interviewing.
Project deep divesNot started
Introductory calls and final interviews probe your experience and area of expertise.
Assessment formats, tool policies and logisticsNot started
AI-tool rules differ by interview and are set in each assessment’s preparation materials.

Confirm with your recruiter

Which skills assessment(s) your team uses (pair coding, take-home project, technical test), how many, and their time limits.
The structure of final interviews: number of sessions, topics and interviewers, and whether they span one or two days (the guide gives a typical 4 to 6 hours with 4 to 6 people).
The level and team the process is calibrated for, and which areas of expertise will be probed.
Which programming languages and environments are expected, especially for pair coding and take-homes.
For each assessment, whether AI tools are allowed or prohibited, since the guide says this varies by interview.
For take-homes: deadline, expected effort, allowed libraries and what to submit.
Logistics: virtual by default or the optional San Francisco onsite, and when references are requested.

Limitations of this page

The live page could not be fetched automatically; the content comes from an archive capture about six weeks old.
The guide is generic across roles; research, engineering and product loops are not described separately.