What are the responsibilities and job description for the Reinforcement Learning & Controls Research Scientist- Spot Behavior position at Boston Dynamics?
At Boston Dynamics, we are pushing the boundaries of what legged robots can do in the real world. The Spot Behavior team is building next-generation locomotion and mobility capabilities, and we are seeking a curious, driven engineer to develop reinforcement learning solutions that run directly on our quadruped platforms. In this role, you will design, train, and deploy RL policies that integrate tightly with Spot's control stack to deliver robust, agile behavior across real-world environments.
Day-to-Day Activities
Day-to-Day Activities
- Design and deploy RL systems that improve Spot's mobility and robustness across challenging terrain.
- Tune and validate low-level controllers (e.g., PD/PID, whole-body control) at the interface with learned policies.
- Build and maintain simulation environments (e.g., Isaac Sim, MuJoCo) to train and validate policies before hardware deployment.
- Analyze robot data logs to diagnose control failures and iterate on controller design.
- Test and debug directly on our in-house Spot fleet, taking a first-principles approach to failure analysis.
- Write production-ready code in Python and C .
- MS or PhD in Robotics, Mechanical Engineering, Computer Science, or related field.
- 3β6 years of experience deploying RL or learning-based control policies on physical hardware.
- Strong foundations in classical control theory, including stability analysis, state estimation, and low-level actuation.
- Experience with real-time software constraints and control loop design.
- Proficiency in Python and C .
- Familiarity with modern deep RL frameworks (e.g., PyTorch, RLlib).
- Experience with legged robotics or contact-rich locomotion systems.
- PhD in a relevant field.
- Familiarity with whole-body control or model predictive control (MPC) for legged systems.
- Experience with state estimators (e.g., EKF, contact estimation) in robotics.
- Experience with sim-to-real transfer and domain randomization.
Salary : $177,000 - $225,000