Robot Learning Intern

Department: Engineering

Location: Singapore Office

Employment Type: FullTime

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

• Develop new algorithms and methods for training AI models that enhance robot dexterity.

• Conduct cutting-edge research across multiple disciplines (Robotics, RL/IL, control, perception, etc.).

• Design and implement state-of-the-art learning-based manipulation/navigation/control algorithms on real robots.

• Work with other teams to develop a diverse set of robust manipulation skills for robots, e.g. VLA, WAM.

Minimum Qualifications

• Currently enrolled in a PhD program or have a master degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field.

• Passionate about working with robots.

• Research experience in embodied AI, robotics, computer vision, machine learning, human-AI interaction, or computer science.

• Experience with deep learning frameworks such as PyTorch.

• Solid understanding of SOTA robot learning techniques (reinforcement learning, imitation learning, etc.).

• Experienced with robot simulators such as Isaac Gym/Isaac Sim/SAPIEN/MuJoCo/Drake, etc.

• Experience building systems based on machine learning and/or deep learning methods.

Preferred Qualifications

• A track record of research, with work published in top conferences and journals such as Science Robotics, IJRR, RSS, CoRL, ICRA, NeurIPS, ICML, ICLR, CVPR, etc.