- Robot type
- Humanoid
- Location
- FremontCaliforniaUSA
- Job type
- Artificial Intelligence
- Posted
- Jan 19, 2026
- Salary
- $120,000–$300,000 a year
Reinforcement Learning Engineer
Job description
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.
Dexmate is building the foundation for physical AI — combining a new generation of robots 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.
Job 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
Job 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.
- Experience with distributed RL training systems
- Experience with sim-to-real transfer techniques
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