Reinforcement Learning Engineer
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Jan 19, 2026. Dexmate scores A on the Alion truth index.
About Dexmate
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. 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.
The Role
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
Minimum 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.)