Robot learning engineer

Company 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.

Responsibilities

Design and implement state-of-the-art learning algorithms for robot manipulation, navigation, and control—from simulation to deployment on physical systems

Develop novel approaches to enhance robot dexterity and mobility using reinforcement learning, imitation learning, and foundation models, etc.

Scale ML systems for large-scale model training and fine-tuning.

Build diverse, robust manipulation skills that push the boundaries of what robots can do

Collaborate closely with hardware, controls, and systems engineers to create integrated solutions

Qualifications

PhD in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or related field; OR Master's degree with 1+ years industry experience; OR Bachelor's degree with 3+ years industry experience

2+ years of hands-on experience developing AI systems for robotics applications

Deep expertise in modern robot learning techniques (reinforcement learning, imitation learning, behavior cloning, etc.)

Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX)

Proven experience conducting real robot experiments and debugging complex robotic systems

Experience with robot simulators (Isaac Gym, Isaac Sim, MuJoCo, SAPIEN, Drake, or similar)

Excellent problem-solving abilities and strong communication skills

Genuine passion for robotics and building products that work in the real world

Preferred Qualifications

Publications at top robotics/ML conferences (RSS, CoRL, ICRA, IROS, NeurIPS, ICLR, etc.)

Experience with vision-language models or foundation models for robotics

Familiarity with sim-to-real transfer techniques and domain randomization

Experience with distributed training and MLOps infrastructure

Background in manipulation, grasping, or mobile manipulation

Track record of taking research from prototype to production