AI Researcher - Robot Learning

What you'll do

• Train and deploy RL policies, and improve sim-to-real transfer for reliable field performance.
• Build training and evaluation infrastructure with benchmarks, regression detection, and links between training metrics and field results.
• Partner with hardware, controls, data, and QA teams to move research into production robot skills.

What they ask for

• Strong Python and/or C++ skills, including experience with large codebases and build tools such as Bazel or equivalent.
• Proficiency with PyTorch for RL policy training and experimentation.
• Hands-on experience with simulation platforms such as Isaac Sim, MuJoCo, or equivalent.
• Demonstrated experience training RL policies for manipulation or locomotion, including addressing the sim-to-real gap on physical hardware.

Nice to have

• Experience with model-based RL, world-model-guided learning, imitation learning, or learning from demonstration.
• Experience deploying RL-trained policies to physical robots in production; background in legged locomotion, dexterous manipulation, or contact-rich control.

Pay

$200,000 - $300,000 USD base salary

Tools

Python, C++, Bazel, PyTorch, Isaac Sim, MuJoCo, PPO, SAC

AI focus

Develop and deploy reinforcement-learning policies for robot manipulation and locomotion, improve sim-to-real transfer, and build policy training and evaluation infrastructure.