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

Robotics Engineer (Aerospace)

Robotics Engineer

Robotics Engineer, Locomotion

Robotics Engineer (Mechanical)