About the job
We are seeking a Sr. Applied Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments.
Responsibilities
- Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors
- Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency
- Use simulation and real-world testing to refine and validate control algorithms
- Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges
- Lead technical projects from conception through production deployment
- Mentor junior scientists and engineers
- Bridge research initiatives with practical engineering implementation
Qualifications
Minimum
- PhD, or Master's degree and 6+ years of applied research experience
- Strong publication record at major Robotics/ML/AI conferences (e.g., RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR)
- Experience with simulation environments for robot learning (Isaac Gym/Lab, MuJoCo, or similar)
- Experience with sim-to-real transfer for robotic systems
- Strong understanding of kinematics and motion planning for robotic systems
- Experience with reinforcement learning, imitation learning, or other AI techniques applied to robotic motor control
Preferred
- History of impactful first-author publications at major conferences
- Experience with large-scale distributed training for RL
- History of technical leadership and cross-functional collaboration
- Experience bridging research with practical engineering implementation in robotics systems
- Experience bridging academic research and production robotics
- Experience integrating perception systems (e.g., vision and touch sensors) into motor control pipelines
- Familiarity with hardware constraints and actuator dynamics for robots