OpenAI
OpenAI Agent Post-Training Interview
Focus areas and question themes aggregated from 5 current openings — pick any opening below and practice a voice mock calibrated to it.
OpenAI Agent Post-Training mock interview
A live voice mock calibrated to this role — real questions, the real follow-up rhythm, and a score at the end. Free to start.
OpenAI's Agent Post-Training family covers several sub-teams -- API and Power Users, Artifacts, Computer Use, Context, and Personality -- that all take agentic model behavior from a qualitative failure to a shipped fix inside major training runs. Regardless of surface area, the loop tests whether you can turn a messy transcript into an eval, a reward signal, and a change that survives the full training stack.
What this interview tests
- Post-training and RL mechanics — Every posting expects fluency with RLHF/RLAIF and post-training pipelines -- data, reward signals, and how a change gets integrated into a major model training run.
- Eval and grader design — A recurring skill is designing evals or graders that reflect real workflows, whether API/power-user tasks, computer-use tasks, or artifact generation, rather than synthetic benchmarks.
- Turning qualitative failures into experiments — Nearly every posting asks you to describe diagnosing a model failure from a transcript or complaint and converting it into training data, a hypothesis, or a concrete fix.
- Domain-specific agent behavior — Each sub-team owns its own surface -- tool use and error recovery for API/power users, structured artifact quality, computer-operating reliability, compute-on-context scaling, or collaborative personality -- and interviewers probe depth in that specific area.
- Cross-functional and cross-stack ownership — The postings repeatedly stress owning work end to end across research, product, infra, and safety boundaries, and distinguishing benchmark movement from real user impact.
Common question themes
Describe a time you diagnosed a model failure from a transcript and turned it into training data or an eval.
This exact framing appears in the API and Power Users posting and echoes the family's emphasis on qualitative-to-quantitative translation.
How do you design an eval or grader that reflects real workflows rather than synthetic tests?
Named explicitly in the API/Power Users and Artifacts postings as a core skill.
Walk through a post-training or RL experiment you ran on an agentic or tool-using model.
Directly asked in the Computer Use Research posting.
How do you decide whether a model improvement is ready to go into a major training run?
Appears across the API/Power Users, Artifacts, Computer Use, and Context postings as a recurring decision point.
How do you avoid collapsing model personality into one narrow style while still fixing a behavior problem?
Specific to the Personality posting's concern with preserving behavioral diversity.
Design an experiment to test scaling the compute spent on context.
Pulled directly from the Context Research posting's focus on compute-on-context scaling.
How do you distinguish benchmark movement from real product or user impact?
Raised explicitly in both the Computer Use and Context Research postings.
Tell me about a project you owned end to end across research, data, and product boundaries.
Cross-functional ownership is called out across nearly all five postings.
Likely format
Interview format isn't specified in any of these postings, so treat this as inferred rather than confirmed. The consistent pattern of walk-me-through and describe-a-time questions paired with technical design asks like design an eval or design an experiment suggests a mix of behavioral-style research narratives and technical discussion of eval/RL design, rather than pure algorithmic coding.
All 5 OpenAI openings in this role
OpenAI
Mid
Agent Post-Training, API & Power Users
OpenAI
Mid
Agent Post-Training, Artifacts Research
OpenAI
Mid
Agent Post-Training, Computer Use Research
OpenAI
Mid
Agent Post-Training, Context Research
OpenAI
Mid
Agent Post-Training, Personality
Frequently asked questions
What's the difference between the Agent Post-Training sub-teams at OpenAI?
They split by the agentic surface each team owns: API/power-user tool use, artifact generation quality, computer-use reliability, compute-on-context scaling, and agent personality or collaboration style. The underlying post-training and eval-design skills overlap, but each expects depth in its specific domain.
Do I need research publications to interview for Agent Post-Training?
The postings don't mention publications as a requirement -- they emphasize hands-on experience with LLMs, RL/RLHF, evals, or production ML systems instead. What matters is being able to describe concrete post-training work you've done, not a publication record.
Is this role more research or more engineering?
It's both, based on the postings -- you're expected to run RL/post-training experiments and build data pipelines and graders, which requires engineering execution, while also making research-style judgment calls about what's actually improving the model versus just moving a benchmark.