Robot Learning Engineer

Responsibilities

• Design, train, and evaluate learned policies for manipulation and whole-body control using imitation learning, reinforcement learning, and VLA-style architectures
• Deploy and validate models on physical robots, tuning for real-world performance
• Iterate rapidly between simulation and real robots through experiment design and data collection
• Own infrastructure for model training, including job scheduling, checkpointing, and scaling distributed training across GPU clusters
• Build research tooling for debugging, visualization, and experiment analysis
• Collaborate with controls, embedded, and hardware teams to translate research into deployable systems

Requirements

• Master's or PhD in Robotics, ML, CS, or related field
• 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 on physical hardware
• Strong ownership mindset for end-to-end system development

Preferred Qualifications

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

About the Company

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.