Introduction#
Most "top company coding questions" lists are the same forty algorithm problems with a different logo on top. That is not useful for OpenAI, because OpenAI's coding rounds are not mainly algorithm rounds.
The questions in ShowOffer's OpenAI bank tell a consistent story. Out of 45 coding questions, the largest groups are stateful systems you implement in code, concurrency and distributed behavior, and debugging existing code — with classic algorithmic problems as a minority. That distribution is the single most useful fact you can know before preparing.
Every question linked below is a real practice problem you can open and run.
These are the question families in our own OpenAI bank, grouped by the skill they test. We are not reproducing anyone's problem list or claiming OpenAI publishes one. Formats vary by team and level — confirm your loop with your recruiter.
The Shape of an OpenAI Coding Round#
1. Stateful Systems You Implement in Code#
The signature OpenAI question. You are asked to build a small working component — not to find a clever trick — and then extend it when the interviewer changes the requirements.
How to practice these: build the simplest correct version first, then — before the interviewer asks — say out loud what would have to change to add snapshots, expiry, or persistence. The extension is the real question, and anticipating it is the strongest signal you can give.
Note how many of these involve snapshots or checkpoints. That is not a coincidence: "now make it versioned" is the most natural extension of any state-holding component, which makes it a favorite follow-up.
2. Concurrency and Distributed Behavior#
The family candidates most often underprepare, and the one that maps directly to what the company actually builds.
How to practice these: narrate shared state explicitly. For each problem, say which state two workers can touch simultaneously, what the worst interleaving does, and why your synchronization is sufficient rather than merely present. A lock you cannot justify is worse than no lock, because it signals pattern-matching.
3. Debug and Optimize#
A distinctly OpenAI-flavored category: you are handed working-ish code and asked to find what is wrong with it.
How to practice these: resist the urge to rewrite. The scored behavior is reading unfamiliar code carefully, forming a hypothesis, and testing it — not replacing the file with your own version. Say "let me reproduce the bug first" and mean it.
The at-least-once pipeline problem is worth special attention. Idempotency bugs are the canonical distributed-systems defect, and being fluent about duplicate delivery puts you ahead of most candidates.
4. ML-Adjacent Numerics#
You do not need to be a researcher. You do need to be unbothered by gradients and loss functions if the team is model-adjacent.
How to practice these: get comfortable writing a backward pass by hand and reasoning about masking. These problems are mechanical once you have done three of them, and intimidating if you have done none — which makes them unusually high-return preparation.
5. Simulation and Object Modeling#
Messy real-world rules, and your job is to turn them into clean structure under time pressure.
How to practice these: spend the first three minutes on the data model, out loud, before writing a method. Candidates who start coding immediately end up with rules scattered across five functions and no way to add the sixth rule the interviewer is about to request.
6. Classic Algorithms (Still Present)#
Not the main event, but not absent either.
A Two-Week Practice Plan#
Write tests for every problem you practice, even when nobody asks. Test coverage is reported as an explicit scoring dimension in OpenAI coding rounds, and it is the single cheapest differentiator available to you.
FAQ#
How many LeetCode problems should I do for OpenAI?#
Fewer than you think, and a different kind. Based on the distribution of questions in this bank, time spent building small stateful components with tests transfers better than additional algorithm volume. Keep your fundamentals warm, but do not make them the plan.
Do I need machine learning knowledge?#
For model-adjacent teams, you should be comfortable with gradients, losses, and vectorized math — the four problems in section 4 cover the realistic bar. For most other software roles, no.
Are these the exact questions OpenAI will ask?#
No, and treat any list that claims otherwise with suspicion. These are practice problems in the families that OpenAI's rounds draw from. The value is in the shape, not in memorizing specific problems.
What is the single most common mistake?#
Going quiet to search for the optimal approach. In a round scored on working code and tests, twenty silent minutes is unrecoverable. Get something running early and say so.