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OpenAI Interview Questions

Reconstructed from 971 verified candidate reports across 100 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.

971
candidate reports
100
distinct questions
5
round types
Monthly
refresh cadence

Key facts

  • •100 distinct OpenAI interview questions indexed
  • •971 candidate reports across the catalog
  • •Most reported: Disease/Epidemic Spreading Simulation — 67× (last seen August 2026)
  • •Reports span Feb 2025 – May 2026
  • •Refreshed monthly · last updated September 2026

Browse OpenAI interviews by topic

Coding Interview QuestionsSystem Design Interview QuestionsBehavioral Interview QuestionsMachine Learning Interview QuestionsInterview Process & Rounds

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 does OpenAI ask in each interview round?

OpenAI interviews span 5 distinct round types, broken down below. 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.

Onsite coding
40 questions

60–75 minute live coding rounds. Multiple sub-problems progressing in difficulty. Test harness usually provided.

Most-reported: Disease/Epidemic Spreading Simulation (67×)
System design
30 questions

60 minute design rounds. Interviewers push hard on the specific dimension their team cares about (storage at scale, real-time fan-out, multi-tenancy).

Most-reported: System Design: Payment System (51×)
Behavioral / culture fit
15 questions

Two-way conversations. Anthropic in particular probes AI safety alignment hard; OpenAI probes mission-fit and shipping velocity.

Most-reported: Technical Deep Dive: Project Presentation (30×)
Technical deep dive
13 questions

Walk the interviewer through a past project end-to-end. Expect to defend technical choices and trace decisions to outcomes.

Most-reported: ML Search / RAG System Design (16×)
Async coding assessment
2 questions

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: Minimum Steps to Target Number via Modular Addition (1×)

Which OpenAI interview questions come up most?

These are the OpenAIquestions reported most across the loops we’ve indexed, sorted by candidate-report frequency.

QuestionRoundReportedLast seen
Disease/Epidemic Spreading SimulationCoding67×August 2026
System Design: Payment SystemSystem Design51×August 2026
System Design: CI/CD PipelineSystem Design48×June 2026
GPU Credit Management SystemCoding47×August 2026
Key-Value Store Design and ImplementationCoding44×August 2026
System Design: SlackSystem Design38×May 2026
Transformer DebuggingCoding37×August 2026
In-Memory SQL / Database ImplementationCoding34×February 2026
Toy Language: Type Inference and ASTCoding32×July 2026
Technical Deep Dive: Project PresentationBehavioral30×August 2026

The full index is below, or browse the OpenAI catalog →

Every OpenAIinterview question we’ve indexed

All 100, grouped by round and sorted by how often candidates reported them. Each links to the question, its reported follow-up count, and when it was last seen.

Coding (40)

System Design (30)

Behavioral (15)

Tech Deep Dive (13)

OA (2)

Read two OpenAI questions free

Full problem statements, candidate-reported follow-ups, and walkthroughs. No signup needed.

Free previewCoding40× reported
GPU Credit Management System

Implement add_credit / charge / get_balance with out-of-order timestamps and earliest-expiring-first depletion. 90 min, test harness provided.

Last reported April 2026Read it →
Free previewSystem Design45× reported
System Design: CI/CD Pipeline

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.

Last reported March 2026Read it →
Free previewCoding37× reported
Transformer Debugging

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.

Last reported August 2026Read it →
What passing candidates do
  • •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
Where candidates lose points
  • •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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