Researcher, Computer Use - Agent Post-Training
Core
Teaching frontier AI models to operate computers, navigate browsers and desktops, use tools, and complete long-horizon tasks with reliability.
Role type
Senior IC researcher (computer use agents)
Builds
Training data, environments, graders, reward signals, and evals for agentic model behavior
Domain
AI research, machine learning, 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, product impact focus, 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, and reward signals; Build evals and environments to expose model failures and convert them into training data; Partner with product teams to translate user needs into model improvements; Work on early-training and alignment interventions including data mixtures and objectives; Improve machinery for large-scale training including experiment velocity and production readiness; Debug hard failures in shipped models and turn qualitative behavior into concrete fixes
