Reinforcement learning engineer
Dexmate · On-site
MeritLog read this listing from Dexmate's Ashby job board and last checked it on September 9, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- On-site
- Salary
- $120K - $300K
- Location
- Fremont Office
- Company website
- customer.io
Hiring context
How this role compares at Dexmate
Dexmate has 39 live roles in MeritLog’s catalog across 9 job families, and 25 of them are in engineering. 32 of those listings publish a pay range, a disclosure rate of 82%.
This role's posted range of $120K - $300K sits above 63% of the 27 other Dexmate roles quoted over the same currency and period.
Dexmate concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- 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
What they're asking for
- Strong experience with reinforcement learning (PPO, SAC, TD3, DDPG, etc.)Skill
- Hands-on experience with robotics systems (simulation or real robots)Skill
- Proven track record applying RL to manipulation, locomotion, or navigation tasksSkill
- Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX)Skill
- Strong understanding of robot kinematics, dynamics, and controlSkill
- Experience with GPU-based simulation such as Isaac Gym, Isaac Lab, SAPIEN, etc.Skill
- Experience with distributed RL training systemsSkillPreferred
- Experience with sim-to-real transfer techniquesSkillPreferred
- Publications in robotics or RL conferences (CoRL, ICRA, RSS, NeurIPS, ICLR, ICML, etc.)SkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
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.)