Reinforcement Learning Engineer

Roboticsconnected; Fremont (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

Dexmate is actively seeking reinforcement learning experts to design and deploy RL algorithms that enhance our robotic platforms. You will work on manipulation, locomotion, and navigation tasks, leveraging simulation and real-world data to push the state of the art.

You will collaborate with robotics engineers, scale training pipelines, and help bring RL models into production as part of a fast-paced, open ecosystem focused on democratizing physical AI.

Qualifications

• Strong experience with reinforcement learning algorithms (PPO, SAC, TD3, DDPG, etc.).
• Hands-on experience with robotics systems and real-world or simulated environments.
• Proficiency in Python and DL frameworks such as PyTorch, TensorFlow, or JAX.
• Solid understanding of robot kinematics, dynamics, and control.
• Experience with GPU-based robotics simulation tools like Isaac Gym, Isaac Lab, or SAPIEN.

Responsibilities

• Design and implement reinforcement learning algorithms for robotics tasks
• Develop and optimize RL training pipelines in simulation and real-world settings
• Collaborate with robotics engineers to integrate RL models into production systems
• Conduct experiments to evaluate and improve algorithm performance
• Scale training infrastructure for learning across multiple robots

Skills

Reinforcement learning

Python

Deep learning frameworks

Robotics systems

Robot kinematics and control

GPU-based simulation

Tools

Isaac Gym

Isaac Lab

SAPIEN

Job description

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.)