Robot Learning Engineer

The Role

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.

Responsibilities

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

Minimum 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 — not just offline benchmarks
• Ownership mindset: you design, build, operate, and iterate on systems end-to-end

Preferred Qualifications

• 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