Lead the development of algorithms for robotic locomotion and manipulation, optimizing policies for real-time execution and sim-to-real transfer.
About Us
We are working on embodied intelligence. Our mission is to scale general-purpose autonomy for real world problems (the 3Ds), through large-scale learning, multi-modal data, and robust control.
We are looking for passionate engineers and scientists who thrive at the intersection of machine learning, robotics, and systems engineering, and want to see their research come alive in real robots.
Role Overview
You will lead development of the algorithms and architectures that enable our robots to achieve stable, responsive, and life-like movement in challenging conditions. This role demands deep knowledge of physical systems, control mechanisms, and foundational AI model research. You will design learning systems that power whole-body locomotion and real-world manipulation.
Responsibilities
- Design and implement models, e.g. RL policies for whole-body locomotion, enabling robots to walk, dance, balance, and recover from disturbances
- Develop novel observation spaces, action representations, and reward functions grounded in fundamental robotics principles
- Create and refine control strategies for real-time execution
- Optimize and evaluate locomotion policies in both simulated environments and on Asimov, our open source, humanoid reference design
- Pioneer techniques to enhance sim-to-real transfer, bridging the gap between virtual testing and physical deployment
- Collaborate closely with simulation, hardware, and autonomy teams to ensure seamless integration of locomotion systems
- Deploy production-ready locomotion policies to our fleet of operational humanoid robots
- Contribute to the advancement of robotics research through publications and open-source contributions
Preferred Qualifications
- BS/MS/PhD in Robotics, AI/Computer Science, or related field
- Solid understanding of robotics fundamentals, including geometry, linear algebra, kinematics, dynamics, probability, and statistics
- Experience working with robotic systems, ideally on legged robotic systems with high degrees of freedom
- Experience implementing control strategies including impedance control, adaptive control, force control, MPC on hardware preferred
- Experience with sim2real techniques OR deep understanding of physics fundamentals
- Familiarity with Machine learning and Reinforcement Learning fundamentals OR strong background in optimization-based planning and control
Bonus Skills
- Work on humanoid locomotion, manipulation, or whole-body coordination
- Prior open-source or research contributions in robotics, control, or deep learning
Top Skills
AI
Control Strategies
Machine Learning
Optimization
Reinforcement Learning
Robotics
Simulation
Menlo Research Singapore Office
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