Reinforcement Learning Engineer
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Before you apply
SalaryNo pay range posted
LocationFremont Office
Work modeon-site
Typefull-time
DepartmentEngineering
Company size51–200 people
PostedJan 19, 2026 · 9mo ago
Verified live1d ago
May no longer be actively hiring
Posted by the company on Jan 19, 202667% of this company's open roles have been listed for 90+ daysNo pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
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
Publications in robotics or RL conferences (CoRL, ICRA, RSS, NeurIPS, ICLR, ICML, etc.)
Skills
Python
PyTorch
TensorFlow
JAX
Isaac Gym
Isaac Lab
SAPIEN
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
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Develop and optimize RL training pipelines in both simulation and real-world environments
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Collaborate with robotics engineers to integrate RL models into production systems
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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.)