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Vice President Deep Reinforcement Learning Jobs (NOW HIRING)

In deep partnership with Ali Noorani, the new President, and passionate colleagues and trustees ... They lead, mentor, manage and inspire the program and learning and evaluation teams, modeling and ...

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... and deep industry expertise, we are at the forefront of industry change. A market leader and ... Job Overview Summary The Vice President of Learning & Development (L&D) is a senior enterprise ...

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... This engineer will leverage their deep expertise in RL to solve critical locomotion and ...

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How much do vice president deep reinforcement learning jobs pay per year?

As of Jun 18, 2026, the average yearly pay for vice president deep reinforcement learning in the United States is $157,532.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,000.00 and $190,000.00 per year, depending on experience, location, and employer.
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Reinforcement learning engineer

Dexmate

Santa Clara, CA

Other

Posted 28 days ago


Job description

Reinforcement Learning Expert

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.)