Agent Post-Training, Computer Use Research
Core
Teaching frontier AI models to operate computers, navigate browsers/desktops, use tools, and complete long-horizon tasks with reliability.
Role type
Senior IC machine-learning engineer (agent post-training, computer use)
Builds
Training signals, evals, environments, and feedback loops for OpenAI's next-generation agents
Domain
AI research, frontier model training, computer use, multi-agent systems
Deliverable
production ML models
Required skills
machine learning fundamentals, software engineering, statistics, LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, production ML systems
Preferred skills
research taste, engineering execution, cross-functional collaboration, building load-bearing systems
Technologies
RL, RLHF, RLAIF, synthetic data, eval loops, production ML systems
Responsibilities
Design and run experiments to improve agentic model behavior for complex computer use; 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 model failures and turn them into training data or product fixes; Partner with product teams to translate user needs into model improvements; Work on early-training and alignment interventions including data mixtures, objectives, and synthetic data; Improve machinery for large-scale training and launch including experiment velocity, reliability, observability, reproducibility, cost, and latency; Debug hard failures in shipped models and turn qualitative behavior into concrete hypotheses and fixes
Seniority
Senior, hands-on IC