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OpenAI

GPT models, ChatGPT and the API platform; frontier research lab

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Interview in a few hours? A quick sprint for OpenAI

AI in interviews

high confidence

It depends on the round: some OpenAI interview formats deliberately allow AI tools, others test you without them. Your prep materials say which applies; ask your recruiter if unsure.

Checked 20 Oct 2026Source

Typical interview loop

How OpenAI usually runs it for applied AI and LLM engineer roles.

  1. Intro call

    Focus: Background, motivation, team fit

    A conversation with a recruiter or the hiring manager about your work, academic background, motivation and goals.

  2. Skills-based assessment

    Focus: Practical coding ability

    A team-dependent technical step such as pair coding, a take-home project or a technical test; some roles get more than one.

  3. Final loop: coding

    Focus: Practical code quality & testing

    Practical, work-like coding (not puzzle-style) where the solution's design, code quality, performance and tests are judged; some candidates also get a piloted round extending an existing codebase with an AI coding agent.

  4. Final loop: system design

    Focus: Architecture & trade-offs at scale

    An hour designing a complete system from scratch, with follow-ups on scale, reliability and trade-offs.

  5. Project deep dive / presentation

    Focus: Ownership & technical depth

    You present a hard technical project you led (slides expected) and get probed on what you personally did and why.

  6. Behavioral interviews

    Focus: Collaboration & mission alignment

    Conversations with a senior manager and teammates about collaboration, past work and your views on safe, beneficial AI.

How it weights your plan

Selecting OpenAI when you build a plan multiplies each topic’s priority score:

Coding+30%
System design+30%
Behavioral+20%
AI coding round+10%

Official prep and resources

Public candidate write-ups

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