Robotics & Reinforcement Learning Engineer
Core
Develop, deploy, and validate reinforcement learning control policies for real robotic systems, bridging simulation and hardware.
Role type
Robotics & Reinforcement Learning Engineer
Builds
Robust locomotion and manipulation policies (walking, balancing, grasping) for physical robots
Domain
Robotics, Machine Learning, Control Theory
Deliverable
production ML models
Required skills
Reinforcement Learning, Sim-to-real transfer, System identification, Policy optimization (PPO, SAC), Imitation learning, Python, C++, ROS 2, MuJoCo, MJLab
Preferred skills
Humanoid/legged robot experience, Actuator modeling, MPC, GPU-based training pipelines
Responsibilities
Design and train locomotion/manipulation policies; Implement RL and imitation learning approaches; Deploy policies on real robot hardware; Bridge sim-to-real gap via domain randomization; Integrate policies into ROS 2 stacks; Analyze failures and iterate models/reward functions
Seniority
Mid-to-Senior, hands-on IC
