Robotics AI Researcher: End-to-End Policy Learning

DeepReach AI; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

DeepReach AI in San Francisco is building the data infrastructure for robot learning. We seek researchers who can train robot manipulation policies, design experiments, and build reproducible training and evaluation frameworks.

Candidates should have advanced degrees or equivalent experience, strong PyTorch, and hands-on policy deployment to real robots. You will work with frontier labs and publish at top venues, owning your work end-to-end.

Qualifications

• MS or PhD in Robotics, Computer Science, Machine Learning, or related field, or equivalent experience.
• Strong PyTorch skills and hands-on experience deploying policies to real robots.
• Familiarity with major open-source imitation learning or VLA frameworks.
• Solid robotics fundamentals: coordinate frames, kinematics, basic control.
• Rigor in evaluation with controlled experiments and statistics.

Responsibilities

• Train robot manipulation policies (imitation learning, diffusion policies, VLAs) on large-scale human demonstration and robot data, and evaluate them in simulation and on real hardware.
• Design systematic experiments to understand data scale, diversity, and composition effects on policy performance.
• Build reproducible training and evaluation frameworks and standardized benchmarks.
• Turn experimental findings into actionable data standards, collaborating with data and engineering teams.
• Track and reproduce state-of-the-art research to keep the team at the frontier.

Skills

PyTorch

Robot policy deployment

Imitation learning / VLA

Robotics fundamentals

Experiment design & statistics

Education

MS/PhD in Robotics/CS/ML or equivalent

Tools

Open-source frameworks

Job description

DeepReach AI in San Francisco is building the data infrastructure for robot learning. We seek researchers who can train robot manipulation policies, design experiments, and build reproducible training and evaluation frameworks.

Candidates should have advanced degrees or equivalent experience, strong PyTorch, and hands-on policy deployment to real robots. You will work with frontier labs and publish at top venues, owning your work end-to-end.