OpenAI Research Scientist Interview Questions (2026)

1. 1

   Tell me about a time a collaborator's or reviewer's critique changed the direction of your research.

   What they're checking: Openness to feedback, one of the four things OpenAI's interview guide says it cares about.

   A strong answer: Say what the critique was, your first reaction, how you tested whether it was right, and what changed in the work. Show that you updated on evidence, not on seniority.

   Common miss: A critique you dismissed and were proved right about, told as a lesson in conviction.

   Likely follow-up: “Tell me about a critique you rejected. How did you decide?”

   Show how to answer

“Tell me about yourself”, “Why here?” and the other common questions, and how to prep each →

1. 1

   Tell me about a research project where you had to overcome significant technical challenges.

   What they're checking: Persistence plus judgment: how you diagnose a blocker and when you change approach.

   A strong answer: STAR. The research goal, the specific technical wall, the hypotheses you tested to get past it, the one that worked, and what you'd do sooner next time.

   Common miss: A list of things that went wrong with no clear decision point.

   Likely follow-up: “At what point did you consider abandoning the project?”

   Show how to answer
2. 2

   Tell me about a research agenda you owned: how you chose the problem and kept a long project moving.

   Senior

   What they're checking: Research ownership. The Research Scientist posting asks for someone who chooses impactful problems and carries out long-running projects on their own.

   A strong answer: STAR. Why that problem over others, the milestones you set to know you were on track, a moment you changed course, and what the project produced.

   Common miss: A project your advisor or manager set, described as if you'd chosen it.

   Likely follow-up: “What problem did you decide not to work on, and why?”

   Show how to answer
3. 3

   Describe a time you collaborated across disciplines to advance a research project.

   What they're checking: Collaboration and clear communication, both named in OpenAI's interview guide.

   A strong answer: STAR. Who you worked with, what each side misunderstood about the other at first, how you closed that gap, and what the combined work achieved that neither side could alone.

   Common miss: Collaboration that meant handing off a task and receiving results.

   Likely follow-up: “What did you learn from their field that changed your own work?”

   Show how to answer

The six topics behind behavioral questions, and how to prep a story for each →

1. 1

   Walk us through an idea of yours that improved on existing ML work. What was new, and how did you show it worked?

   Senior

   What they're checking: Originality with evidence, which the posting frames as first-author publications or projects.

   A strong answer: State the prior method and its limit, your idea in one sentence, the experiment that isolated its effect, the baseline you beat and by how much, and where it still fails. Make your own contribution explicit.

   Common miss: A result without an ablation, so nobody can tell which change mattered.

   Likely follow-up: “What's the strongest reason a reviewer might not believe your result?”

   Show how to answer
2. 2

   Explain how you would design and evaluate a large-scale experiment for fine-tuning a language model.

   What they're checking: Experimental discipline at scale. The RL/Reasoning posting prizes "simple experiments in tightly-controlled settings."

   A strong answer: Frame the hypothesis and the one variable you're changing. State what you'll hold fixed (data, seeds, compute). Run small-scale checks before the big run, pick evaluation sets you didn't tune on, and name the trade-off between breadth and cost. Say which result would make you stop.

   Common miss: Changing several things at once and attributing the gain to the most interesting one.

   Likely follow-up: “Your gain disappears with a different random seed. What now?”

   Show how to answer
3. 3

   How would you evaluate whether an RLHF reward model has been overfit or is exhibiting reward hacking?

   Senior

   What they're checking: Whether you know how optimization pressure breaks proxies, and how to detect it.

   A strong answer: Frame the gap between reward and true quality. Assume you have held-out human judgments. Track reward against human preference as optimization increases, look for divergence, and inspect high-reward samples for exploits. Name the trade-off in regularization strength, pick a stopping rule, and say how you'd confirm it.

   Common miss: Checking reward-model accuracy on its own validation set and calling it done.

   Likely follow-up: “Human raters also prefer the exploit. Is it still reward hacking?”

   Show how to answer
4. 4

   You join a team with a large ML codebase, and a training run's loss curve looks wrong. How do you debug it?

   What they're checking: Hands-on debugging in someone else's code. The RL/Reasoning posting asks for comfort "diving into a large ML codebase to debug and improve it."

   A strong answer: Frame what "wrong" means (divergence, plateau, spikes). Reproduce at small scale, then check data, learning rate schedule, numerical precision and recent code changes, in that order. Overfit a tiny batch to rule out the model and optimizer. Say when you'd ask the code's owner instead of digging.

   Common miss: Tuning hyperparameters until the curve looks fine without finding the cause.

   Likely follow-up: “It only happens at full scale. What changes in your approach?”

   Show how to answer
5. 5

   What are the key trade-offs between scaling model size vs. improving training efficiency?

   What they're checking: Whether you reason about compute budgets quantitatively rather than by slogan.

   A strong answer: Frame the fixed budget. State your assumption about how loss scales with parameters and data. Compare spending on a bigger model, more data, or better methods, including the inference cost of a bigger model. Make a call for a specific budget and say which small experiment would test it.

   Common miss: Saying "it depends" without naming what it depends on.

   Likely follow-up: “How would inference cost change your answer?”

   Show how to answer

1. 1

   A pre-training run is showing unexpected capability emergence in a domain you didn't target. How do you decide whether to continue, pause, or publish?

   Senior

   What they're checking: Judgment under uncertainty when a result has safety as well as research implications.

   A strong answer: Frame the decision and who else owns it. Verify the capability is real with evaluations you trust, assess its risk, and escalate early. Name the trade-off between speed and caution, recommend a path, and say what evidence would change it.

   Common miss: Deciding alone, or treating it purely as an exciting result to publish.

   Likely follow-up: “Your evaluations disagree with each other. What do you report?”

   Show how to answer