Research Scientist, Reinforcement Learning - Atlas

What You’ll Do

• Design, implement, and train reinforcement learning algorithms for challenging whole-body mobile manipulation and bimanual manipulation tasks.
• Develop high-quality Python and C++ code that is tested, documented, and production-ready.
• Build and leverage high-fidelity simulation environments (e.g., Isaac Sim, MuJoCo) to validate RL policies before deploying on hardware.
• Integrate learned policies with Atlas’s control and software stack through close collaboration with controls and platform teams.
• Deploy, debug, and iterate policies directly on real Atlas hardware through hands-on experimentation.
• Participate in design reviews, experimental planning, and team-wide research direction.

We’re Looking For

• MS or PhD in Computer Science, Machine Learning, Robotics, or a related field.
• Strong experience training and deploying RL policies for complex behaviors in robots or simulated agents.
• Proficiency with modern ML frameworks (e.g., PyTorch, TensorFlow, RLlib).
• Strong foundations in algorithms, debugging, performance optimization, and robotics fundamentals (kinematics, dynamics).
• Excellent Python and C++ programming skills and experience contributing to production-scale software.
• Nice to Have: PhD or equivalent research experience in reinforcement learning or robotic manipulation.
• Experience deploying RL policies on physical robots.
• Experience developing locomotion, bimanual manipulation, or whole‑body control behaviors.
• Contributions to large software projects or open-source ML/robotics frameworks.
• Publications in top-tier robotics or ML conferences (e.g., CoRL, RSS, ICRA, NeurIPS).

Benefits

Boston Dynamics offers a generous benefits package including medical, dental, vision, 401(k), paid time off, and an annual bonus structure.

Compensation

The base pay range for this position is between $175,000 to $230,000 annually. Base pay will depend on multiple individualized factors including, but not limited to, internal equity, job related knowledge, skills and experience. This range represents a good faith estimate of compensation at the time of posting.