Role Overview
We're seeking Reinforcement Learning experts to develop and deploy cutting-edge RL algorithms that enhance our robots' capabilities.
Responsibilities
Design and implement reinforcement learning algorithms for various robotics tasks
Develop and optimize RL training pipelines in both simulation and real-world environments
Collaborate with robotics engineers to integrate RL models into production systems
Conduct experiments to evaluate and improve algorithm performance
Scale training infrastructure for efficient learning across multiple robots
Required Qualifications
Strong experience with reinforcement learning (PPO, SAC, TD3, DDPG, etc.)
Hands-on experience with robotics systems (simulation or real robots)
Proven track record applying RL to manipulation, locomotion, or navigation tasks
Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX)
Strong understanding of robot kinematics, dynamics, and control
Experience with GPU-based simulation such as Isaac Gym, Isaac Lab, SAPIEN, etc.
Preferred Qualifications
Experience with distributed RL training systems
Experience with sim-to-real transfer techniques
Publications in robotics or RL conferences (CoRL, ICRA, RSS, NeurIPS, ICLR, ICML, etc.)
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