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:
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++.
We are looking for:
MS or PhD in Robotics, Mechanical Engineering, Computer Science, or related field.
3–6 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).
Nice to Have:
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.
The base pay range for this position is between $177,000 to $225,000 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. Boston Dynamics offers a generous Benefits package including medical, dental vision, 401(k), paid time off and a annual bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer for employment.
Skills Required
- MS or PhD in Robotics, Mechanical Engineering, Computer Science, or related field
- 3-6 years 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
- Proficiency in C++
- Experience building and maintaining simulation environments (e.g., Isaac Sim, MuJoCo)
- Familiarity with modern deep RL frameworks (e.g., PyTorch, RLlib)
- Experience analyzing robot data logs and debugging on hardware
- 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)
- Experience with sim-to-real transfer and domain randomization
What We Do
Boston Dynamics builds advanced mobile manipulation robots with remarkable mobility, dexterity perception and agility. We use sensor-based controls and computation to unlock the potential of complex mechanisms. Our world-class development teams develop prototypes for wild new concepts, do build-test-build engineering and field testing and transform successful designs into robot products. Our goal is to change your idea of what robots can do.
