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Do you have a passion for applying machine learning to drive real-world robot behavior? As a Reinforcement Learning Engineer on the Spot Behavior team, you will develop and deploy cutting-edge reinforcement learning techniques to expand Spot’s capabilities in dynamic, real-world environments. You’ll work on a multidisciplinary team tackling high-impact mobility challenges—ranging from terrain traversal and balance to complex locomotion behaviors. This role offers the opportunity to work hands-on with Spot and push the boundaries of legged robot performance.
Job Responsibility:
Design and deploy reinforcement learning systems to improve Spot’s mobility and robustness
Integrate learning-based solutions into Spot’s existing planning and control systems in collaboration with experts across controls, perception, and planning
Build and maintain systems that support reliable, scalable, and reproducible RL training
Test and debug your work using our in-house fleet of Spot robots
Write high-quality, maintainable code in both Python and C++
Provide mentorship and technical guidance on ML best practices
Requirements:
Master’s degree or higher in Robotics, Mechanical Engineering, Computer Science, or a related field
3+ years of experience with a proven track record of deploying models on hardware
Proficiency in both Python and C++ programming languages
Strong analytical and debugging skills
Familiarity with modern deep RL toolkits and architectures
Nice to have:
Experience with legged robotics
PhD in Robotics, Mechanical Engineering, Computer Science, or a related field