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
Senior IC applied scientist (reinforcement learning & robotics)
Builds
Robotic systems for automation and human-robot collaboration
Domain
Robotics, AI, Control Systems
Deliverable
production ML models
Required skills
reinforcement learning, imitation learning, hierarchical quadratic programming, model-predictive control, real-time controller development, state estimation from multiple sensors
Preferred skills
low-level joint torque/impedance control, teleoperation systems, robotics frameworks for fast prototyping
Technologies
IsaacLab, Mujoco, Drake, ROS, Matlab, Java, C++, Python
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; Mentor junior engineers and scientists
