Research Engineer at OpenAI
How ready are you for the OpenAI ML loop? 12 questions calibrated to what they actually probe.
Take the readiness check
12 questions across the 6 topics OpenAI's ML loops actually probe. Two are spot-the-bug code questions. Your answers save locally; you can come back anytime.
How the loop runs
OpenAI publishes the process explicitly. Five stages, the recruiting team aiming for one week between each. The skills assessment format depends on the team. The final loop is 4 to 6 hours over 1 to 2 days with 4 to 6 interviewers.
01Application & resume review
~1 week
Application & resume review
~1 week
Recruiting reviews your application. Not credential-driven; they want to understand your background and what you would contribute.
02Introductory call
30 to 45 min · Recruiter or hiring manager
Introductory call
30 to 45 min · Recruiter or hiring manager
Conversation about work, motivations, and goals. OpenAI explicitly recommends reading their recent blog posts before this call.
03Skills-based assessment
~1 week to next stage · Pair coding, take-home, or technical test
Skills-based assessment
~1 week to next stage · Pair coding, take-home, or technical test
For ML roles this is typically ML coding in NumPy or PyTorch — implementing attention, KV cache, layer norm, or small training loops. Recruiter sends prep before this stage.
04Final interviews
4 to 6 hours over 1 to 2 days · Virtual default, SF onsite optional
Final interviews
4 to 6 hours over 1 to 2 days · Virtual default, SF onsite optional
Production-quality coding, ML system design, ML debugging on a non-learning training loop, and a project deep-dive presentation.
05Decision and references
~1 week
Decision and references
~1 week
OpenAI commits to responding within a week of the final loop. Recruiter may ask for references.
Worth knowing
- Recruiter sends prep materials before the skills assessment. Read them carefully. First-person accounts cite candidates failing because they skipped or skimmed it.
- Bring slides for the project deep-dive — first-person accounts describe it as a job-talk format, ~10 slides defending technical choices on past work.