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The OpenAI interview process varies by role and team. Rehearse connected technical, project, and people stages with independent practice aligned to public role signals.
A research-oriented road from recruiter calibration through ML technical screens, domain design, research presentation, team conversations, and hiring manager.
A recruiter screen followed by the reported front-loaded MLE block before final project, design, and manager gates.
The commonly reported recruiter, coding-screen, and final-loop sequence for software engineering candidates.
The reported infrastructure loop with separate ML and systems coding, followed by system design, manager, and technical deep dive.
A practical product-engineering path with coding, live build, product system design, mission/values, and team matching.
A connected original practice path whose eleven stages are also reusable standalone Question Bank exercises.
The complete released and Question Bank collection for this company.
These posts do not name a role.
The post names both a research role and an ML role.
Preparation focus—not a claimed OpenAI interview sequence, a fixed stage list, or a list of previously asked questions. Formats vary by role and team.
Prepare for an introductory conversation, a skills-based assessment whose format depends on the team, and a final stage reported at roughly four to six hours across one or two days, with a decision normally within about a week. A recent software-engineering report describes a practical build screen, a timed refactor or debug round, system design, a project deep dive, and a hiring-manager conversation; treat that variant as role-dependent rather than universal.
Rehearse progressive coding, research-engineering system design, AI infrastructure, inference APIs, and LLM evaluation through original work-like exercises.
Practice research discussion, project deep dives, hiring-manager judgment, and behavioral evidence as connected but distinct interview skills.