How OpenAI interviews
These are the OpenAI loops we have researched. Each one lists the rounds, what they test, the follow-up questions to expect, and how to prepare, with the sources we used.
- ML Infrastructure Engineer
- Senior · 6 rounds · 6 h
- Product Manager
- Senior / L5 · 4 rounds · 3 h 20 min
- Product Designer
- Senior · 6 rounds · 4 h 30 min
What OpenAI says about its interviews
OpenAI lists pair coding, take-home projects and technical tests as possible skills assessments. OpenAI Interview Guide
OpenAI ML Infrastructure Engineer interview
Prepare for practical coding and a final loop of systems, technical depth, and collaboration interviews. For ML infrastructure, center preparation on reliable training and inference: resource scheduling, data movement, checkpoint recovery, utilization, and latency. Strong answers turn ambiguous requirements into a working design, quantify bottlenecks, and connect low-level symptoms to system behavior. Bring a project deep dive and a specific account of learning quickly under pressure.
The 6 rounds
Practical coding assessment
Produce a clear stateful implementation with explicit behavior checks and a defensible performance argument.
Expect: One resumable iterator or bounded-state component with incremental requirements.
Follow-ups: “What happens if progress is restored after the input changes?” “Can memory remain bounded over a long run?”
Final coding: progressive implementation
Maintain a coherent implementation while supporting a sequence of new requirements.
Expect: One data-processing component extended with queries, ordering or deletion.
Follow-ups: “What changes when queries become much more frequent than writes?” “How do removed entries affect iteration or snapshots?”
Training and inference systems design
Design one training or inference workflow from submission to durable completion, then defend its hardest resource or failure boundary.
Expect: An ML workload platform examined through scheduling, data flow and recovery.
Follow-ups: “What happens when a worker disappears during checkpointing?” “How does the design trade latency for batching efficiency?”
Technical project and ML infrastructure depth
Demonstrate technical depth by explaining a real infrastructure mechanism and the measurements that guided a decision.
Expect: A technically detailed infrastructure project. A training or serving bottleneck with quantitative diagnosis. A resource trade-off defended against an alternative.
Follow-ups: “Which measurement would falsify your diagnosis?” “What changes between training and online inference?”
Infrastructure failure diagnosis
Practice the cross-layer debugging expected by the ML infrastructure role while keeping mitigation and root-cause reasoning distinct.
Expect: A training regression investigated through competing hypotheses. An inference latency regression with a resource bottleneck. A stalled workload requiring safe recovery.
Follow-ups: “What if utilization is high but progress is low?” “How would you distinguish a numerical issue from bad restored state?”
Collaboration, learning and mission
Demonstrate effective collaboration and a specific motivation for this infrastructure work through real decisions and outcomes.
Expect: Rapid learning in an unfamiliar technical area. Feedback that changed a consequential decision. Work beyond your formal responsibility.
Follow-ups: “What did you change after critical feedback?” “How did you know you had learned enough to act?”
What candidates and sources report
- OpenAI lists pair coding, take-home projects and technical tests as possible skills assessments. OpenAI
- OpenAI describes final interviews as typically 4–6 hours with 4–6 people across one or two days. OpenAI
- The engineering guide evaluates solution design, code quality, performance and test coverage. OpenAI
- The guide explicitly values communication, collaboration, openness to feedback and rapid learning. OpenAI
- OpenAI says AI and other tool permissions differ by interview format and are specified in preparation materials. OpenAI
How to prepare
- Implement resumable state: Build a resumable iterator or bounded-state component. Specify restore semantics and manually trace boundary cases.
- Extend a data engine: Add queries, ordering and deletion to one small component. Preserve earlier behavior and explain index trade-offs.
- Design the workload lifecycle: Model a training or inference job from admission to durable completion. Quantify one resource bottleneck and one failure boundary.
- Prepare a technical deep dive: Choose a project with measurements and a consequential decision. Rehearse mechanism-level questions and alternative designs.
- Diagnose a cross-layer failure: Investigate a stalled workload through competing compute, data, numerical and coordination hypotheses; define safe recovery evidence.
- Ground mission and collaboration: Read the interview guide and relevant team work. Prepare examples of rapid learning, useful feedback and personal initiative.
Mistakes to avoid
- Do not hide state semantics inside a library call.
- Do not claim coverage from one happy-path example.
- Do not optimize an unproven implementation.
- Do not let a new operation silently change existing semantics.
- Do not list ML infrastructure products without defining their role.
Questions to ask your interviewers
- Which bottleneck most limits useful training or inference throughput today?
- How does this team divide responsibility between researchers and infrastructure engineers?
- What does a successful first infrastructure project look like for this role?
OpenAI Product Manager interview
Expect practical, OpenAI-specific product cases rather than a uniformly calibrated textbook loop. A recent Senior/L5 candidate reports two one-hour cases and a possible case-project presentation; OpenAI says assessments vary by team. Practice launch decisions where model quality, safety, latency, cost, and adoption conflict, and bring evidence of driving complex cross-functional work.
The 4 rounds
Model launch case
Determine whether the candidate can turn uncertain model capability into a safe, measurable product launch.
Expect: Launch a new model into an existing product under quality, cost, and safety constraints.
Follow-ups: “What changes when the model is worse for one important segment?” “Which gate would stop the launch?”
AI product strategy case
Test whether the candidate can choose a product bet when user value, economics, and uncertain capability pull apart.
Expect: Choose between product surfaces or segments for an emerging AI capability.
Follow-ups: “How would a tenfold cost increase change the bet?” “What evidence would reverse your choice?”
Case project presentation
Assess whether a product recommendation survives concise presentation and cross-functional challenge.
Expect: Present and defend a prepared product recommendation without relying on visible slides.
Follow-ups: “Which assumption is least secure?” “How would research or safety challenge this plan?”
Execution and mission
Verify consequential ownership, learning, and mission-aware execution through distinct past experiences.
Expect: Cross-functional launch with material disagreement. A consequential failure and changed operating approach. Rapid learning in an unfamiliar technical domain.
Follow-ups: “What did you personally decide?” “What would the dissenting partner say?”
What candidates and sources report
- OpenAI says assessment formats vary by team and candidates may complete more than one skills assessment. OpenAI
- OpenAI explicitly evaluates collaboration, effective communication, openness to feedback, mission alignment, and rapid domain learning. OpenAI
- One verified Senior/L5 PM candidate completed two one-hour case interviews before the final stage. Candidate report
- That candidate was asked how they would launch a new model in ChatGPT and handle an extreme loss scenario. Candidate report
- The current Core Models PM role balances model quality and usefulness with latency, safety, reliability, and cost. Job posting
How to prepare
- Practice model launch decisions: Run a launch case with quality, latency, safety, and cost in conflict; choose gates and a rollback trigger.
- Choose an AI product bet: Compare two users or surfaces, expose capability assumptions, and commit using a measurable decision rule.
- Rehearse the case defense: Give a concise recommendation aloud, then answer challenges without relying on a visible deck.
- Prepare consequential ownership stories: Bring launch, failure, disagreement, and rapid-learning examples with exact decisions and outcomes.
Mistakes to avoid
- Do not assume model behavior is deterministic.
- Do not hide cost or safety behind adoption metrics.
- Do not list segments without choosing one.
- Do not treat novelty as user value.
- Do not narrate a deck the room cannot see.
Questions to ask your interviewers
- Which product decision will this role own in its first six months?
- How does this team balance offline evaluations with real user outcomes?
- Which final format and artifact rules apply to this opening?
OpenAI Product Designer interview
A recent candidate-and-interviewer guide describes a hiring-manager portfolio review followed by five approximately 45-minute onsite rounds: portfolio presentation, portfolio follow-up, AI whiteboarding, app critique, and behavioral. The strongest evidence is detailed but comes from one publisher, so confirm the schedule. Prepare a clear shipped-work narrative, AI-first interaction reasoning, and design choices that survive latency, scale, and ambiguity challenges.
The 6 rounds
Hiring manager portfolio review
Establish whether one shipped project demonstrates difficult problem framing, excellent execution, and clear ownership.
Expect: Deep walk-through of one ambiguous shipped product.
Follow-ups: “Which alternative did you reject?” “Did the design actually improve the outcome?”
Portfolio presentation
Test whether the candidate can make complex product work understandable and persuasive to a mixed panel.
Expect: Present one or two linked projects as a clear narrative without visible-slide dependence.
Follow-ups: “What is the one decision the panel should remember?”
Portfolio decision defense
Determine whether portfolio decisions hold under product, performance, responsiveness, and scale constraints.
Expect: Focused challenge of one portfolio project's weakest decision.
Follow-ups: “What latency or scale assumption mattered?” “What would you change now?”
AI product whiteboard
Observe first-principles product framing and meaningful AI interaction design under ambiguity.
Expect: Design an AI-assisted experience for a familiar human activity. Reframe a conventional workflow around uncertain AI capability.
Follow-ups: “How does the user recover from a wrong result?” “What should remain under user control?”
App critique
Assess design judgment across information architecture, interaction, copy, motion, business goals, and measurable improvement.
Expect: Critique one familiar product journey and prioritize a change. Explain the business and user logic behind one controversial interface choice.
Follow-ups: “Why does the current choice exist?” “How would you know the change helped?”
Ambiguity and collaboration
Verify how the designer works across disagreement, incomplete data, and a difficult outcome.
Expect: Cross-functional disagreement over direction. Decision made with insufficient data. A project that went wrong and changed the designer.
Follow-ups: “What evidence did you lack?” “How did your behavior change afterward?”
What candidates and sources report
- A recent design guide reports a 30–45 minute hiring-manager portfolio review after portfolio screening. Independent guide
- The same guide reports five approximately 45-minute onsite rounds: presentation, follow-up, whiteboarding, app critique, and behavioral. Independent guide
- The portfolio presentation reportedly emphasizes a compelling storyline more than slide decoration. Independent guide
- The follow-up reportedly tests whether decisions withstand questions about responsiveness, scalability, latency, and actual outcomes. Independent guide
- Reported whiteboard prompts ask candidates to shape ambiguous products that use AI meaningfully. Independent guide
How to prepare
- Sharpen one portfolio story: Rehearse one ambiguous shipped project from problem through decision, trade-off, outcome, and lesson.
- Practice the panel narrative: Present the same work clearly without visible slides, then answer why each consequential choice was made.
- Design AI from first principles: Map a user goal, model uncertainty, control, feedback, and recovery before drawing the main flow.
- Critique decisions, not pixels: Choose one app journey, infer the current decision logic, prioritize one change, and define success.
- Prepare low-ego conflict evidence: Bring distinct disagreement, insufficient-data, and failure stories with your behavior and learning visible.
Mistakes to avoid
- Do not tour every screen.
- Do not blur your work into the team's output.
- Do not describe visuals the panel can already see.
- Do not bury the outcome after process detail.
- Do not protect the artifact at all costs.
Questions to ask your interviewers
- Which product area and seniority will this loop calibrate toward?
- What portfolio format and confidentiality boundaries should candidates follow?
- How does this team evaluate AI-first interaction thinking?
Sources
Research reviewed . Practice exercises are our own; they are not confidential interview questions.
- OpenAI Interview Guide — OpenAI
- OpenAI Software Engineer, RL Training Infrastructure — Job posting
- OpenAI SWE candidate: coding, design and technical deep dive — Candidate report
- Meta E4 candidate account including OpenAI design interview — Candidate report
- Product Manager, Core Models — Job posting
- OpenAI Product Manager, ChatGPT Interview Experience — Candidate report
- OpenAI Product Manager Interview Guide — Independent guide
- OpenAI Product Designer Interview Guide — Independent guide
- OpenAI Product Design Careers — OpenAI
Independent preparation using OpenAI as a practice target. Not affiliated with, endorsed by, or sponsored by OpenAI.