The OpenAI Coding Interview: What They Test

Open the CoderPad tab and there is no problem statement waiting that you can match to a LeetCode tag. What you get is a build task that grows under you. You might start with something like a versioned key-value store, then be asked to serialize it to disk without a JSON library, then to handle concurrent writers. The specifics vary from loop to loop; the escalating build is the constant.

The interviewer adds the next requirement once you clear the last one, so the round behaves like an escalating obstacle course, usually around four gates, and clearing two of them is a pass. Your code runs the whole time. A version that compiles and passes the first case is only the warm-up; the real grading starts there.

There is no LeetCode frequency spine here. The handful of standard algorithm problems that surface in candidate accounts each show up once, never twice, which is its own kind of finding. The signal lives in the build tasks themselves, since repeated problem IDs barely exist here. So do not spend a week hunting for a secret list of OpenAI mediums that does not exist. The loop also forks by track, and which fork you are on decides almost everything about what to drill.

The OpenAI angle

Production quality and your own tests are graded directly, the same as a correct answer. OpenAI's published criteria ask for well-designed solutions, high-quality code, optimal performance, and good test coverage. In practice that means edge cases handled before anyone prompts you for them, names and structure that read cleanly, and tests you write yourself even when the prompt never asked. A working solution with no tests and a tangle of special cases can still fail the round. Treat "make it run, then make it solid, then prove it with tests" as the actual rubric, because for this loop it is.

How the Round Runs

Live rounds happen in CoderPad over video, and your code executes against cases the whole session. There is no whiteboard pseudocode. Some pipelines add an upfront HackerRank assessment (camera on, full screen shared), mostly for new-grad, non-US and Solutions roles. Experienced US software hires usually go straight to a live screen. The counts below flex by team and track, so read them as a rough shape to expect. OpenAI publishes little about its process, and the process changes faster than at FAANG, so confirm the details of your own loop with your recruiter.

• Platform: CoderPad, code runs. Live rounds run in CoderPad over Zoom or Meet, in the language you choose, with code executing against tests throughout. Candidates report typing the entire hour and finishing with cases passing. No whiteboard.
• Shape: ~4 escalating gates. A base build plus stacked follow-up requirements added mid-round. Reported as roughly four gates where clearing two is a pass. You are graded on how far you get with clean, running code; a single optimal answer is beside the point.
• Online assessment: Some pipelines only. A HackerRank test, ~110 minutes for two questions, camera on and full screen shared. Mostly for new-grad, non-US, and Solutions pipelines; experienced software hires often skip it and start with a live screen.
• Two tracks: SWE vs Research Engineer. Software and applied engineers build working systems. Research Engineers implement and debug model internals in Python. Confirm which track your loop is calibrated for; it changes what to practice.

The OpenAI angle

Whether AI assistants are allowed in the coding rounds is unsettled, so confirm it with your recruiter before you assume either way. A few accounts describe being permitted to use an AI tool if you share your screen and narrate; other guides advise planning to work entirely from memory with no references. OpenAI has not published a formal policy, and it likely varies by role and pipeline. Do not build your prep around the assumption. Separately, OpenAI has begun running a new agentic-coding round where steering an AI agent against a real codebase is the task itself, graded on how you direct and verify the agent. The ordinary coding rounds stay hands-on, so prepare for both without assuming either is on your loop.

The SWE Track: Build a Working System

For software and applied roles the prompt is to implement something work-like, often built around stubbed services or rebuilding the behavior of a real system. The recurring data stores are a versioned or time-based key-value store and a Premium question that parses a slice of SQL. Mini systems: a Premium question that propagates formulas and catches circular references, and a Premium question that walks dot, dot-dot and symbolic links without looping forever. Concurrency and state: a Premium question that suspends and resumes, and a multithreaded web crawler. Concurrency and object-oriented design surface far more here than in a typical FAANG loop. With low sample sizes overall, the versioned KV store is the closest thing to a signature; most other prompts are seen once or twice.

Take the versioned store, since it anchors the track. The ask is small, and the rubric is everything around it. Think clear structure, a binary search instead of a linear scan once you handle reads at a timestamp, and edge cases you cover before being told to.

The build itself is forty seconds of work. The round is the next forty minutes: the interviewer adds tombstone deletes, then on-disk serialization that has to survive delimiters inside keys, then concurrent access. Each layer wants the same discipline, name things well, handle the empty and boundary cases, and write a test that proves the new behavior before moving on.

The Research Engineer Track: Implement the Model

Research Engineer coding is a different axis. The work is ML implementation in Python, live and collaborative, leaning on PyTorch or raw NumPy: data processing, tensor manipulation, model-utility code, debugging, refactoring. Multi-head attention written from scratch, without the library's MultiheadAttention call, is now table stakes, sometimes called the hello-world of modern AI interviews. Beyond it: a full transformer block, a training loop, gradient descent, with the building blocks expected from memory, projection layers, residual connections, LayerNorm, causal versus bidirectional attention, an optimizer step.

The round candidates remember most is a debugging one. You are handed a four-hundred-line model that compiles, runs, and quietly refuses to learn, and you have to find why the loss stays flat. The planted bugs are reported as the dumb-mistake kind: a mask not set to negative infinity, a missing loss.backward() or optimizer.zero_grad(), wrong projection dimensions, a double softmax before cross-entropy. You play the role of an engineer debugging a real training run that diverged. Underneath the implementation work sits a math gate, with the softmax and cross-entropy gradient derivation reported to filter a large share of candidates. Of everything on this track, attention-from-scratch and the won't-learn debug round are the two genuinely recurring items; the rest is well-corroborated in type, less so in exact frequency.

How to Practice for This

Grinding three hundred LeetCode problems is close to the wrong preparation. The muscle this loop tests is building a small thing end to end, under time, with tests and clean structure, which is different from pattern-matching a graph problem to a template. So practice by building.

Implement a KV store with versioning, then bolt on serialization and a lock. Write a tiny SQL evaluator that handles WHERE and ORDER BY and a tombstone delete. Build an iterator that suspends and resumes. Do each one in a timer, make it run, and write the tests yourself before you call it done. For the Research Engineer track, the equivalent is implementing attention and a training loop from memory until they are reflex, then reading model code closely enough to catch a missing backward pass.

The OpenAI angle

Narrate as you build, because the round is collaborative and the interviewer is grading the judgment you voice. Say what you are about to implement, name the edge cases you are choosing to handle now versus defer, and call out a trade-off when you make one. When a follow-up gate lands mid-round, restate it back before you code, so the interviewer can correct a wrong assumption early instead of watching you build the wrong thing. The candidates who stall usually go quiet and try to hold the whole design in their head. The ones who clear gates think out loud and keep the code running at every step.

What This Section Covers

Six lessons follow, each one anchored on a build you implement. The first three carry the SWE track and the next two the Research Engineer track, and the last covers the new agentic round and the production-quality discipline that runs across all of them. Where a lesson rests on a base structure, the foundational build lives in the free course, so these pages stay on what is specifically OpenAI.

Common questions