Agent Post-Training, Artifacts Research

Responsibilities

• Design and run experiments to improve agentic model behavior for complex software and plugins
• Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, and reward signals
• Build evals and environments to expose model failures and convert them into training data or research directions
• Partner with Codex and ChatGPT product teams to translate user needs into model improvements
• Work on early-training and alignment interventions, including synthetic data and eval loops
• Improve machinery for large-scale training, including experiment velocity, reliability, and observability
• Debug hard failures in shipped models and turn qualitative behavior into concrete hypotheses and fixes

Requirements

• Strong technical fundamentals in machine learning, software engineering, systems, or statistics
• Hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, or production ML systems
• Experience with evals, graders, synthetic data, or coding/tool-using agents
• Ability to move from vague behavioral problems to concrete experiments and pipelines
• Comfortable working across research, product, infrastructure, and safety boundaries
• Prior background in consulting, finance, marketing, operations, or data science is a plus

About the Company

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity.