Embodied AI Engineer - Controls

Develop, implement, and validate advanced control and learning algorithms for real-world embodied robotic systems.

Design and conduct experiments to expand control robustness, precision, and adaptability across diverse tasks and environments.

Combine classical and learning-based control methods (e.g., MPC, IL, RL) for scalable and reliable skill acquisition.

Collaborate with perception and systems engineers to integrate AI control stacks into production platforms.

Leverage simulation, digital twins, and expert demonstrations to accelerate control policy development and deployment.

Stay up-to-date on cutting-edge research in control theory, reinforcement learning, and embodied AI.

Proven experience delivering production-level robotic control systems in real-world deployments (e.g., autonomous vehicles, manipulators, humanoid or mobile robots).

Strong foundation in modern control techniques (e.g., MPC, adaptive control, system identification) and their integration with learning-based methods.

Deep understanding of reinforcement learning, imitation learning, and optimization for dynamic systems.

Proficiency in Python and familiarity with C++ for real-time robotics applications.

Experience working with high-fidelity simulators (e.g., Isaac Sim, Omniverse, Mujoco) for control development and testing.

Excellent communication and teamwork skills, with the ability to bridge between AI research and robotic systems engineering.