dexmate - Robot Learning Engineer
Requirements
• 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
Responsibilities
• 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. • Robot Learning & Policy Development • 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
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