OpenAI Interview Preparation for Engineering Roles

What OpenAI's public interview guide establishes

Prepare for an OpenAI engineering interview by practicing the complete act of solving a problem: understand the contract, choose a design, implement it, test it, and explain the trade-offs. An exact-question list cannot tell you whether your reasoning will survive a follow-up or an unfamiliar codebase.

OpenAI's official interview guide says assessments vary by team and can include pair coding, projects completed outside the interview, or technical tests. It identifies code quality, performance, testing, communication, and collaboration as engineering considerations. The guide also says tool and AI permissions depend on the interview format. Confirm your own assessment details with the recruiter instead of assuming that tools allowed in one session are allowed in another.

The material below is AlgoDrill's preparation framework, not an account of a private OpenAI interview. We do not claim that OpenAI asks the example exercise or uses our practice rubric.

Prepare for the specific engineering role

Start a one-page preparation brief with three columns: work named in the job description, evidence from your own experience, and skills you need to refresh. For each requirement, select something you can demonstrate rather than a term you can define. “Reliable services” might become an incident analysis with a measurable failure mode and a testable mitigation.

Separate the common coding foundation from your specialization. A product engineer should be ready to discuss interfaces, user-visible failures, and maintainable implementation choices. An infrastructure engineer should rehearse resource limits, observability, concurrency, and recovery decisions. For an ML-focused role, add the modeling, experimentation, numerical, or systems topics actually named in the role. These are proposed study directions, not universal interview requirements.

Choose one project you know deeply. Explain what you owned, what the team owned, how you evaluated success, and where the first design failed. Bring a concrete example of changing your mind after new evidence. Avoid making your contribution sound larger than it was; precise ownership gives an interviewer something useful to explore.

Original practice: deduplicate retry requests

Original AlgoDrill exercise, not a reported OpenAI question: given request records in arrival order, return the first record for each request ID while preserving arrival order. Each record has a string id and a string payload. Later records with an already-seen ID are retries and must be ignored, even if their payload differs. Empty IDs and empty payloads are valid. Do not modify the input.

For [{id: "r2", payload: "A"}, {id: "r1", payload: "B"}, {id: "r2", payload: "C"}], return the first two records, in that order. For an empty list, return an empty list. Before implementing, explain why “keep the last record” and “sort by ID” both violate this contract.

A set of seen IDs and an output list are sufficient. Visit each record once, append only unseen IDs, and mark each appended ID as seen. After any prefix of the input, the output must contain exactly the first occurrence of each ID in that prefix, in the same relative order. Under expected constant-time hash lookup, processing is O(n) time and uses O(u) additional storage, where u is the number of distinct IDs, excluding the input records' payload storage.

Now add an engineering follow-up: you must process an unbounded stream with a memory limit. The original “remember every ID forever” requirement is incompatible with bounded memory unless another storage layer preserves that history. Discuss retention windows, external state, or a changed guarantee explicitly. Do not silently evict old IDs and continue claiming perfect deduplication.

Review correctness and trade-offs

Use the exercise to practice a test plan before optimizing. Include no records, one record, all identical IDs, alternating duplicate IDs, empty strings, and a later duplicate with a changed payload. Check that the original list remains unchanged. Also ask whether records themselves may be shared with the caller or must be copied; the answer changes the output ownership contract.

Review the implementation in three passes. First, find a concrete input that could violate its invariant. Second, inspect the interface and error policy. Third, identify the actual bottleneck before proposing a more complex data structure. For production design practice, describe persistence and concurrency separately from the in-memory algorithm.

Run a practical preparation cycle

1. Clarify: restate the input contract and ask about constraints before choosing a structure.
2. Build: implement a complete, straightforward version without an open solution.
3. Test: run your own boundary cases and explain the result of each.
4. Change: introduce one new constraint and identify which assumption breaks.
5. Recall: return later and reconstruct the invariant and critical lines without notes.

For a short baseline, try the free Two Sum recall drill after reviewing the hash-map guide. Then solve a fresh problem without guided blanks. Recall practice measures one component of readiness; it does not replace independent implementation, design discussion, or specialist preparation.

Before the interview, prepare questions about the team's actual work and the assessment format. Ask which tools are permitted, whether tests can be run, and how to handle an unfamiliar library. Use your recruiter's current instructions as the operational source of truth.

Sources and disclosure

Source checked November 2, 2026: OpenAI's official interview guide. This guide deliberately avoids promising a fixed round count, universal schedule, or a particular question set. The deduplication task and study recommendations are original AlgoDrill material. AlgoDrill is an independent preparation product and is not affiliated with OpenAI.