Robot Learning Engineer

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.

We're developing end-to-end learned models for general-purpose robot manipulation and control, and you'll help build and shape the foundational systems that put capable robots into the real world. This is a broad role that can be tailored to your area of expertise — policy learning, training infrastructure, or real-time inference. You'll work across the full robot learning pipeline: ingesting and processing multimodal data, training large-scale models, deploying policies to physical hardware, and closing the loop between research and real-world performance.

• Design, train, and evaluate learned policies for manipulation and whole-body control — imitation learning, reinforcement learning, and VLA-style architectures

• Deploy and validate models on physical robots, tuning for real-world performance and closing the loop between policy, controller, and hardware

• Iterate rapidly between simulation and real robots — design experiments, collect data, debug failure modes, and drive measurable improvements

• Own infrastructure for model training: job scheduling, checkpointing, metrics, and logging; scale distributed training across GPU clusters

• Build research tooling for debugging, visualization, and experiment analysis

• Work closely with controls, embedded, and hardware teams to translate research needs into reliable, deployable systems

• 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 — not just offline benchmarks

• Ownership mindset: you design, build, operate, and iterate on systems end-to-end

• 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) is a strong plus