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OpenAI Interview Questions

Reconstructed from 873 verified candidate reports across 91 questions. Feb 2025 – Sep 2026.

This page is a live view of every OpenAI interview question AceOffer has indexed — pulled from 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.

AceOffer is not affiliated with, endorsed by, or sponsored by OpenAI. Questions are reconstructed from interview experiences candidates shared, not from OpenAI materials.

What OpenAI pays: reported offers →
873
candidate reports
91
distinct questions
5
round types
Monthly
refresh cadence

Key facts

  • •91 distinct OpenAI interview questions indexed
  • •873 candidate reports across the catalog
  • •Most reported: System Design: Payment System — 53× (last seen September 2026)
  • •Round mix across candidate reports: coding 48%, system design 33%, behavioral 12%, technical deep-dive 7%
  • •Most common coding topics: stateful class design (6 questions), parsing structured text (5 questions), simulation and game rules (4 questions)
  • •43 of the 91 questions were last reported in Jul–Sep 2026
  • •Reports span Feb 2025 – Sep 2026
  • •Refreshed monthly · last updated September 2026

Browse OpenAI interviews by topic

The OpenAI loop, from candidate reports

OpenAI's loop is typically a recruiter chat → phone screens (for software engineers most often one coding round and one system design round, about 60 minutes each; ML roles get ML coding) → a 4–5 round virtual onsite with a hiring manager round at the start or end. The ML-debugging round uses a test harness: passing the tests matters, but the interviewer is grading whether you can explain WHY the bug breaks the model, not just whether the checks go green. System design rounds run about 60 minutes in most reports, and the senior bar is high.

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
34 questions

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

Most-reported: Disease/Epidemic Spreading Simulation (52 reports)
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 (53 reports)
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 (31 reports)
Technical deep dive
10 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 reports)
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.

Which OpenAI interview questions come up most?

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

QuestionRoundReportedLast seen
System Design: Payment SystemSystem Design53×September 2026
Disease/Epidemic Spreading SimulationCoding52×September 2026
GPU Credit Management SystemCoding50×September 2026
System Design: CI/CD PipelineSystem Design48×June 2026
System Design: SlackSystem Design38×May 2026
Transformer DebuggingCoding35×September 2026
Toy Language: Type Inference and ASTCoding34×July 2026
Technical Deep Dive: Project PresentationBehavioral31×September 2026
In-Memory SQL / Database ImplementationCoding29×March 2026
Key-Value Store Design and ImplementationCoding28×August 2026

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

Every OpenAI interview question we’ve indexed

All 91, 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 (34)

System Design (30)

Behavioral (15)

Tech Deep Dive (10)

OA (2)

Read two OpenAI questions free

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

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

Get the full OpenAI catalog

Every question. Every candidate-reported follow-up. The mistakes that sink people, and what passers do instead. Monthly refresh.

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Send them this guide: 91 questions from 873 candidate reports, with the rounds, the follow-ups and what passers do.

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