OpenAI interview questions for engineers

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CANDIDATE-REPORTED · INDEPENDENT SYNTHESIS

What candidates report about the interview

18 reviewed signals

Use these reports to choose what to rehearse, not to predict a fixed loop. Formats vary by role, team, and date; confirm your current process with the recruiter. Frontier Rehearsal is independent and not company-endorsed.

Practice promptsOriginal exercises shaped by recurring preparation themes—not reported interview questions.3

Practice prompt (not a reported interview question): choose a practical developer tool or data structure, implement a usable core in 45 minutes, then diagnose one failure and add a feature while keeping the design testable.
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Practice prompt (not a reported interview question): build a small working service from an incomplete specification. State your assumptions, ship the simplest correct version, then explain how you would test and extend it when the requirements change.
Open permanent link
Practice prompt (not a reported interview question): implement a stateful component in stages. After each working version, handle a new requirement without breaking earlier behavior, and explain the trade-offs behind each revision.
Open permanent link

Process signalsReported stages, formats, timing, and role-specific variations.13

According to candidate reports, OpenAI coding rounds are multi-part exercises that start simple and layer on complexity, while system-design discussions have covered retrieval-augmented chatbots, payment flows and chat-system architecture.
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Candidates report that the OpenAI MLE phone screen presents two problems, each split into several parts that must be completed within the call; interviewers are sometimes willing to overlook an edge-case failure when the overall approach is sound.
Open permanent link
According to a candidate report, OpenAI offered a choice between a traditional 60-minute coding screen and a 60-minute agentic-coding screen in which AI tools are allowed and the task is to navigate, plan and validate changes in an unfamiliar Python codebase.
Open permanent link
Candidate reports describe the OpenAI hiring-manager screen as a management-skills interview driven by past-experience behavioural questions, run at a brisk pace with little introductory conversation.
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According to candidate reports, a failed OpenAI interview typically leads to a cooldown of roughly a year, and repeat candidates are given lower priority than first-time applicants.
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According to a candidate report, OpenAI onsite loops for machine-learning roles can include two separate ML coding rounds.
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Candidate reports indicate that the OpenAI phone screen pairs a coding exercise with a system-design exercise, and that recruiters have invited a candidate to redo the system-design portion after a strong coding signal.
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Candidate reports say OpenAI and Anthropic favour practical coding over LeetCode-style algorithm puzzles, and that behavioural or culture rounds test alignment with company values; examples cited include building a small SQL engine or a key-value store with disk persistence.
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Candidates report that the OpenAI virtual onsite has four rounds in total, one of which is coding, and that a weak single round is sometimes weighed against the rest of the packet rather than treated as an automatic rejection.
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According to candidate reports, the OpenAI first-round screen combines a system-design segment with a coding segment, and the coding prompt arrives with unusually long instructions even though the underlying task is fairly standard.
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Candidates report that OpenAI onsite dates are booked through a self-service tool that limits how far ahead slots can be reserved, with some candidates emailing the recruiter to request later dates; a preparation window of about three weeks is commonly discussed.
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Candidate reports indicate that OpenAI product-manager interviews include separate product-sense and product-execution rounds.
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Candidate reports describe OpenAI's phone-screen bar as demanding: coding answers are expected to complete every part almost flawlessly, and system-design answers are expected to justify design choices in depth.
Open permanent link

Candidate briefingsCompact, independently written summaries of fuller candidate timelines.1

Candidate reports describe the OpenAI SWE loop as a phone screen with a 60-minute coding round and a 60-minute system-design round, then a virtual onsite adding a further coding round, a further system-design round, a technical deep dive and a behavioural interview; coding prompts are often applied builds such as a key-value store with time-based expiry or a rate limiter.
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Discussion themesWhere reports agree, differ, or reveal team-level variation.1

Candidates describe OpenAI and Anthropic onsites as a mix of coding, system design and behavioural or project-deep-dive conversations, and note that strong technical rounds alone may not carry an offer when the behavioural and ownership signals are weak.
Open permanent link

Evidence reviewed Nov 12, 2026. Reports can become stale.

Question groups

Topics

Independent and not affiliated with the companies represented. Collected material does not predict future interview questions.

1 question

Every question has a public brief with the task, constraints, and follow-ups. Labels describe interactive practice access.

Public brief

Topic tags
Concurrency
Storage & state
Security

OpenAISystem designAI-generated

Discussion / designResponse prompt

Reported interview periodFeb 2026–Sep 2026

Design an Online Chess Service

Design a service that matches two players, maintains authoritative legal game state, supports clocks, and allows games to be reviewed later.

Practice focusRequirements - Define APIs or real-time events for matchmaking, moves, resignations, draw offers, reconnects, and spectators. - The server—not the client—validates turns, legal moves, check, checkmate, stalemate, and special rules. - Make duplicate or reordered client messages idempotent. - Maintain fair clocks despite network delay and disconnects. - Persist an append-only move history and enough state for fast reconnect and review. - State scale assumptions, latency goals, and consistency boundaries.…

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CANDIDATE-REPORTED · INDEPENDENT SYNTHESIS

What candidates report about the interview

18 reviewed signals

Practice promptsOriginal exercises shaped by recurring preparation themes—not reported interview questions.3

Practice prompt (not a reported interview question): choose a practical developer tool or data structure, implement a usable core in 45 minutes, then diagnose one failure and add a feature while keeping the design testable.
Open permanent link
Practice prompt (not a reported interview question): build a small working service from an incomplete specification. State your assumptions, ship the simplest correct version, then explain how you would test and extend it when the requirements change.
Open permanent link
Practice prompt (not a reported interview question): implement a stateful component in stages. After each working version, handle a new requirement without breaking earlier behavior, and explain the trade-offs behind each revision.
Open permanent link

Process signalsReported stages, formats, timing, and role-specific variations.13

According to candidate reports, OpenAI coding rounds are multi-part exercises that start simple and layer on complexity, while system-design discussions have covered retrieval-augmented chatbots, payment flows and chat-system architecture.
Open permanent link
Candidates report that the OpenAI MLE phone screen presents two problems, each split into several parts that must be completed within the call; interviewers are sometimes willing to overlook an edge-case failure when the overall approach is sound.
Open permanent link
According to a candidate report, OpenAI offered a choice between a traditional 60-minute coding screen and a 60-minute agentic-coding screen in which AI tools are allowed and the task is to navigate, plan and validate changes in an unfamiliar Python codebase.
Open permanent link
Candidate reports describe the OpenAI hiring-manager screen as a management-skills interview driven by past-experience behavioural questions, run at a brisk pace with little introductory conversation.
Open permanent link
According to candidate reports, a failed OpenAI interview typically leads to a cooldown of roughly a year, and repeat candidates are given lower priority than first-time applicants.
Open permanent link
According to a candidate report, OpenAI onsite loops for machine-learning roles can include two separate ML coding rounds.
Open permanent link
Candidate reports indicate that the OpenAI phone screen pairs a coding exercise with a system-design exercise, and that recruiters have invited a candidate to redo the system-design portion after a strong coding signal.
Open permanent link
Candidate reports say OpenAI and Anthropic favour practical coding over LeetCode-style algorithm puzzles, and that behavioural or culture rounds test alignment with company values; examples cited include building a small SQL engine or a key-value store with disk persistence.
Open permanent link
Candidates report that the OpenAI virtual onsite has four rounds in total, one of which is coding, and that a weak single round is sometimes weighed against the rest of the packet rather than treated as an automatic rejection.
Open permanent link
According to candidate reports, the OpenAI first-round screen combines a system-design segment with a coding segment, and the coding prompt arrives with unusually long instructions even though the underlying task is fairly standard.
Open permanent link
Candidates report that OpenAI onsite dates are booked through a self-service tool that limits how far ahead slots can be reserved, with some candidates emailing the recruiter to request later dates; a preparation window of about three weeks is commonly discussed.
Open permanent link
Candidate reports indicate that OpenAI product-manager interviews include separate product-sense and product-execution rounds.
Open permanent link
Candidate reports describe OpenAI's phone-screen bar as demanding: coding answers are expected to complete every part almost flawlessly, and system-design answers are expected to justify design choices in depth.
Open permanent link

Candidate briefingsCompact, independently written summaries of fuller candidate timelines.1

Candidate reports describe the OpenAI SWE loop as a phone screen with a 60-minute coding round and a 60-minute system-design round, then a virtual onsite adding a further coding round, a further system-design round, a technical deep dive and a behavioural interview; coding prompts are often applied builds such as a key-value store with time-based expiry or a rate limiter.
Open permanent link

Discussion themesWhere reports agree, differ, or reveal team-level variation.1

Candidates describe OpenAI and Anthropic onsites as a mix of coding, system design and behavioural or project-deep-dive conversations, and note that strong technical rounds alone may not carry an offer when the behavioural and ownership signals are weak.
Open permanent link

Evidence reviewed Nov 12, 2026. Reports can become stale.

Question groups

Topics

Independent and not affiliated with the companies represented. Collected material does not predict future interview questions.

1 question

Every question has a public brief with the task, constraints, and follow-ups. Labels describe interactive practice access.

Public brief

Topic tags
Concurrency
Storage & state
Security

OpenAISystem designAI-generated

Discussion / designResponse prompt

Reported interview periodFeb 2026–Sep 2026

Design an Online Chess Service

Design a service that matches two players, maintains authoritative legal game state, supports clocks, and allows games to be reviewed later.

OpenAI interview questions for engineering roles

OpenAI interview questions for engineering roles

What candidates report about the interview

Design an Online Chess Service

What candidates report about the interview

Design an Online Chess Service