Research Scientist, Reinforcement Learning - Atlas

Boston Dynamics•Waltham, MA

1d•Onsite

About The Position

At Boston Dynamics, we are pushing the boundaries of what advanced humanoid robots can do in the real world. The Atlas team is building next-generation whole-body mobile manipulation capabilities, and we are seeking a curious, driven Research Scientist to develop cutting-edge reinforcement learning (RL) solutions that run directly on our humanoid platforms. In this role, you will design, train, and deploy RL policies that combine whole-body movement and dexterous manipulation to solve complex tasks in unstructured environments. You’ll work with a world-class team of roboticists and have rare, direct access to our physical Atlas robots and large-scale simulation infrastructure.

Requirements

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 Haves

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).

Responsibilities

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.