OpenAI Interview Questions
Reconstructed from 843 verified candidate reports across 97 questions. Feb 2025 – May 2026.
This page is a live view of every OpenAI interview question AceOffer has indexed — pulled from real candidate reports, not invented from job descriptions or one founder’s memory. Every question shows how many times it’s been reported and when it was last seen. The catalog gets a refresh pass every month.
Key facts
- •97 distinct OpenAI interview questions indexed
- •843 candidate reports across the catalog
- •Most reported: Disease/Epidemic Spreading Simulation — 51× (last seen July 2026)
- •Reports span Feb 2025 – May 2026
- •Refreshed monthly · last updated July 2026
Browse OpenAI interviews by topic
The OpenAI loop, from candidate reports
OpenAI's loop is typically a recruiter chat → 1–2 phone screens (60–75 min each, coding or ML coding) → a 4–5 round virtual onsite with a hiring manager round at the start or end. Coding rounds use a test harness — passing the tests matters but the interviewer is grading whether you can verbalize WHY the bug breaks the model, not just whether you can make the green checkmark appear. System design rounds at OpenAI run shorter than typical (~45 min on-design, ~15 min Q&A) and the senior bar is high — multiple candidates specifically reported being asked to walk through stuck-state recovery, exactly-once semantics, and real-time log streaming on the same round, all within the same hour.
What gets asked, by round
Counts reflect distinct questions per round, not number of times asked. Frequencies on individual question cards show how many candidates reported getting that specific question.
60–75 minute live coding rounds. Multiple sub-problems progressing in difficulty. Test harness usually provided.
60 minute design rounds. Interviewers push hard on the specific dimension their team cares about (storage at scale, real-time fan-out, multi-tenancy).
Walk the interviewer through a past project end-to-end. Expect to defend technical choices and trace decisions to outcomes.
Two-way conversations. Anthropic in particular probes AI safety alignment hard; OpenAI probes mission-fit and shipping velocity.
Take-home or proctored 90-minute online assessment before the loop. Used as a filter — not weighted in the final decision once you're in the onsite.
Most reported OpenAI questions
Sorted by candidate-report frequency. These are the questions that have recurred most across the loops we’ve indexed.
| Question | Round | Reported | Last seen |
|---|---|---|---|
| Disease/Epidemic Spreading Simulation | Coding | 51× | July 2026 |
| System Design: CI/CD Pipeline | System Design | 48× | June 2026 |
| GPU Credit Management System | Coding | 43× | June 2026 |
| System Design: Payment System | System Design | 39× | June 2026 |
| Key-Value Store Design and Implementation | Coding | 39× | May 2026 |
| System Design: Slack | System Design | 38× | May 2026 |
| Transformer Debugging | Tech Deep Dive | 36× | April 2026 |
| In-Memory SQL / Database Implementation | Coding | 34× | February 2026 |
| Toy Language: Type Inference and AST | Coding | 31× | May 2026 |
| Technical Deep Dive: Project Presentation | Behavioral | 25× | June 2026 |
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Implement add_credit / charge / get_balance with out-of-order timestamps and earliest-expiring-first depletion. 90 min, test harness provided.
Design a multi-tenant CI/CD system triggered by git push. The interviewer probes hard on idempotency, real-time log streaming, and stuck-state recovery.
Debug a nanoGPT-style PyTorch transformer with 4 canonical bugs: positional embedding init, causal mask without -inf, output projection dim, and a training-loop / label-shift error. Follow-up: implement KV cache. The #1 most-reported OpenAI ML coding round.
- •Verbalize the formula or invariant being violated for every bug fix in transformer/ML debug rounds — green tests aren't enough; the interviewer grades on the WHY
- •Clarify scope before designing — CI/CD prompts often have an unmentioned constraint (jobs are shell scripts, not K8s; workflows are linear, not DAGs); passers ask, fail-cases over-engineer
- •For coding rounds with test harnesses: read all the test cases before writing code — they reveal implicit requirements not in the written prompt (especially GPU Credit, Toy Language)
- •Carry a canonical-formula cheat sheet into ML debug rounds: sinusoidal PE, scaled attention with /√d_k, LayerNorm axis, cross-entropy with shift — pattern recognition is the win
- •Demonstrate quantitative reasoning explicitly — multiple system-design passers reported doing back-of-envelope math (QPS, latency, storage) before proposing a solution
- •Modifying code until the test harness goes green without identifying which canonical formula was violated — fails the verbal Q&A even if tests pass
- •Over-engineering CI/CD or system design when the interviewer explicitly simplified scope ("jobs are just shell scripts, no K8s needed")
- •Skipping the multi-tenancy / fairness discussion in system design — comes up in CI/CD, Slack, Payment, ChatGPT UI specifically
- •Treating GPU Credit as a sweep-line problem when Version II requires event-replay (subtract permanently depletes earliest-expiring grants)
- •On ML coding: not knowing why ReLU's expected variance changes (Kaiming vs Xavier) — interviewers will probe init-scheme choices
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