Agent Post-Training, Personality
Help shape OpenAI agent personality by turning qualitative collaboration insights into evals, training data, reward signals, and model improvements that reach production.
About the job
Responsibilities
- Develop a rigorous understanding of what makes an agent a great collaborator across professional, creative, technical, and everyday work.
- Turn qualitative judgments about model behavior into concrete hypotheses, evals, graders, and training interventions.
- Study explicit and implicit user signals to understand which behaviors create trust, satisfaction, continued use, and successful outcomes.
- Work with human experts and trainers to produce high-quality, tasteful rollouts and preference data that capture excellent collaborative behavior.
- Improve reward models and RL objectives for model behaviors.
- Work with pretraining and early-training teams on data mixtures, objectives, synthetic data, and other upstream choices that shape downstream personality.
- Build sustainable pipelines for updating older training data as our understanding of excellent model behavior evolves.
- Partner closely with ChatGPT, Codex, and other product teams to turn consumer insight into model improvements and validate them in real workflows.
- Own projects end to end, from observing a subtle behavioral failure through experimentation, training, evaluation, and launch.
Requirements
- Strong technical foundations in machine learning, software engineering, statistics, behavioral science, HCI, or a related field.
- Strong taste for model behavior: can look at user feedback and explain why one response feels thoughtful, natural, and useful while another does not.
- Experience with LLMs, post-training, RL/RLHF, reward modeling, evals, synthetic data, pretraining data, or production ML systems.
- Ability to work effectively with researchers, engineers, product teams, designers, domain experts, human-data teams and safety boundaries, and communicate clearly with each group.
- Ability to translate subjective-seeming product questions into falsifiable hypotheses and rigorous evaluations without losing the nuance that made the question important.
- Care about preserving individuality, adaptability, and behavioral diversity rather than optimizing every model toward one narrow style.
- Excited by ambiguous capability problems where the signal is noisy, the failures are qualitative, and the solution may involve data, training, evals, product changes, or all of the above.
- Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.
Nice-to-Haves
- Think instinctively from the user’s perspective and care deeply about how models feel to work with, not only how they perform on benchmarks.
- Want to shape how frontier agents communicate, collaborate, and build trust with millions of people.
- Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.
Skills
Machine Learning, Software Engineering, Statistics, Behavioral Science, Hci, LLMs, Post-Training, Rl, RLHF, Reward Modeling, Evals, Synthetic Data, Pretraining Data, Production Ml Systems
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