Reinforcement learning engineer

Job details

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On-site

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Not listed by source

Location

Fremont Office

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Job description

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. Today, robotics is fragmented, slow, and closed: most builders are forced to reinvent the same stack again and again, and most ideas never make it past the prototype stage. We exist to change that. Our mission is to democratize robotics by lowering the barrier to entry, delivering a plug-and-play platform for developers, researchers, and enterprises, and cultivating an open ecosystem that accelerates the evolution of physical AI. If you want to help shape the next layer of human capability - and believe the future of robotics should be built together, not in isolation - we'd love to build it with you. 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.)