Agent Post-Training, Artifacts Research

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. As a member of the Agent Post-Training, Artifacts team, you will train frontier models to create polished, useful work products and improve agent behavior through various experiments and collaborations.

Responsibilities:

• Design and run experiments that improve agentic model behavior for complex software and plugins
• Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis
• Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions
• Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements
• Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior
• Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs
• Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness
• Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments
• Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes