Robot Learning Engineer

The role

You will develop end-to-end learned models for general-purpose robot manipulation and control. You will work across the robot learning pipeline including data processing, model training, and deployment to physical hardware.

What you will do

• Design, train, and evaluate learned policies for manipulation and whole-body control
• Deploy and validate models on physical robots
• Iterate between simulation and real robots
• Own infrastructure for model training
• Scale distributed training across GPU clusters
• Build research tooling for debugging, visualization, and experiment analysis
• Work with controls, embedded, and hardware teams

Required qualifications

• Master's or PhD in Robotics, ML, CS, or related field, or equivalent practical experience
• Hands-on experience training and deploying ML models, ideally in PyTorch
• Background in robot learning, imitation/reinforcement learning, or learning-augmented control
• Experience taking models from research to deployment
• Ownership mindset

Nice to have

• Experience with VLAs, video generation architectures, or robot foundation models
• Experience deploying learned policies on physical robots — manipulation, legged, or humanoid systems
• Experience with serialization formats for high-performance systems (Protobuf, MCAP)
• Track record of publications at top-tier venues (ICRA, IROS, CoRL, RSS, NeurIPS, ICML)

About the company

Dexmate is building the foundation for physical AI by combining a new generation of robots with a universal Physical AI OS. Their mission is to democratize robotics by delivering a plug-and-play platform for developers, researchers, and enterprises.