OpenAI
Mid
Research role shaping how OpenAI's agents collaborate, communicate, and build trust with users
The Agent Post-Training Personality team defines what makes an agent a thoughtful, tasteful collaborator across Codex, ChatGPT, and the API — going beyond writing style to how agents understand intent, ask questions, and take initiative. The role spans behavioral research, evals, training data, and reward signals, working across post-training and pretraining to ship personality improvements into production models. It requires strong technical foundations plus genuine taste for what makes model behavior feel right.
Step into this interview
Free · a live voice mock calibrated to this exact role
What this interview tests
- Translating qualitative behavior judgments into evals and hypotheses
- LLM post-training: RLHF, reward modeling, preference/synthetic data
- Preserving behavioral diversity vs. optimizing to one style
- Cross-functional collaboration with product and human-data teams
- End-to-end ownership from observed failure to shipped model improvement
Common question themes
Design an eval for a subjective agent behavior like 'asks good clarifying questions'
Walk through a training signal or reward model you built or improved
How do you avoid collapsing model personality into one narrow style
Tell me about turning a vague user complaint into a concrete model fix that shipped
How do you validate that a personality improvement survives the full training stack, not just an offline eval
What makes one model response feel thoughtful and another not, and how would you operationalize that judgment
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