Reinforcement Learning Engineer

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

Requirements

• 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.)

About the Company

Dexmate is building the foundation for physical AI — a unified platform that combines high-quality robotic hardware with a universal Physical AI OS, making robots as easy to build and deploy as software.