OpenAI Software Engineer Interview Process Retrospective: Including an Optional Agentic Coding Round
A 2026 OpenAI SWE interview retrospective shows that the process includes a recruiter screen, two technical screens of about 60 minutes each on the same day, a take-home assignment of about 48 hours, a technical deep dive that probes the code line by line, and 4 onsite rounds. The onsite coding includes a progressive multi-part problem (such as token-level streaming diff and rollback), and the system design question is "Design ChatGPT," involving GPU allocation and autoscaling under non-stationary traffic. There is also an optional fifth round, Agentic Coding, in the beta stage, which requires using an AI coding agent to handle problems in large codebases that are difficult to address by hand.
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OpenAI Software Engineer Interview Transcript
Reposted from Reddit r/InterviewCoderHQ, a personal recap of a 2026 OpenAI SWE interview: the process places more emphasis than typical big tech on "practical system problems + take-home assignments + line-by-line follow-up questions on code," and the onsite also includes progressive multi-part coding; there is also an optional Agentic Coding round (using an AI coding assistant to tackle problems too large to handle by hand).
# Process Overview
1. Recruiter Screen A fairly relaxed background chat. Was asked about views on the future of AI; recommends reading OpenAI's Charter beforehand.
2. Technical Screen (two sessions on the same day, about 60 minutes each) · Coding: build a versioned key-value store. · System design: a job scheduler with fault tolerance.
3. Take-Home (about 48 hours) Requires "building something real": a distributed webhook delivery system, including retries and dead-letter queues.
4. Technical Deep Dive The interviewer first reads through the assignment, then makes a list and goes through the code line by line: every choice and every decision may be questioned.
5. Onsite Loop (4 rounds total)
Coding 1: a progressive multi-part problem, where each part must first have a working version before the next part is opened. An example problem is a token-level streaming diff, requiring tracking state changes and supporting rollback. Advice: get a correct version working as early as possible, then iterate.
Coding 2: more systems-flavored, covering state management, concurrency, and memory efficiency; also tests Python internals, such as generators, async, and iterators.
System design: the problem is "design ChatGPT." Focus areas include GPU allocation, autoscaling under non-stationary traffic, and distributed coordination. Advice: abstract out the model serving layer; for the behavioral portion, prepare stories about technical leadership, cross-team architecture decisions, and reaching consensus under pressure, and give concrete trade-offs rather than vague soft-skills talk.
6. Agentic Coding Round (beta, optional fifth round) Given a very large codebase and a problem that is "too hard to handle by hand," the expectation is to solve it using an AI coding agent.