Applied Scientist II, Reinforcement Learning
Core
Design and implement whole body control methods for balance, locomotion, and dexterous manipulation in advanced robotics systems.
Role type
Applied Scientist II, Reinforcement Learning
Builds
Adaptable automation solutions capable of working safely alongside humans in dynamic environments
Domain
Robotics, AI, Control Systems
Deliverable
production ML models
Required skills
reinforcement learning, imitation learning, hierarchical quadratic programming, model-predictive control, real-time controller implementation, state estimation from multiple sensor modalities, simulation environment development
Preferred skills
Java, C++, Python, low-level joint torque/impedance control, teleoperation systems, robotics frameworks for fast prototyping
Technologies
IsaacLab, Mujoco, Drake, ROS, Matlab
Responsibilities
Design and implement whole body control methods for balance, locomotion, and dexterous manipulation; Utilize state-of-the-art methods in learned and model-based control; Create robust and safe behaviors for different terrains and tasks; Implement real-time controllers with stability guarantees; Collaborate with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation; Mentor junior engineer and scientists
Seniority
Senior, hands-on IC with research leadership
