Research Scientist, Reinforcement Learning - Atlas 

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.

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

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 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

Direct access to cutting-edge humanoid robots and large-scale RL infrastructure; highly collaborative, mission-driven team; opportunity to define state-of-the-art humanoid capabilities; medical; dental; vision; 401(k); paid time off; annual bonus