6 months ago
Remote, United States or Seattle, WA, USASenior
Responsibilities
- Develop, implement, and validate advanced control and learning algorithms for real-world embodied robotic systems.
- Design and conduct experiments to improve control robustness, precision, and adaptability across tasks and environments.
- Combine classical and learning-based control methods, including MPC, imitation learning, and reinforcement learning, for scalable skill acquisition.
- Collaborate with perception and systems engineers to integrate AI control stacks into production platforms.
- Use simulation, digital twins, and expert demonstrations to accelerate control policy development and deployment.
- Stay current with research in control theory, reinforcement learning, and embodied AI.
Requirements
- At least 5 years of industry experience.
- Proven experience delivering production-level robotic control systems in real-world deployments, such as autonomous vehicles, manipulators, humanoid robots, or mobile robots.
- Strong foundation in modern control techniques, including MPC, adaptive control, and 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 with high-fidelity simulators such as Isaac Sim, Omniverse, or MuJoCo.
- Strong communication and teamwork skills, including the ability to bridge AI research and robotic systems engineering.
Categories
ML EngineeringRobotics
