⭐ Robot Learning Researcher/Engineer (Imitation Learning, Foundation Models, RL)

Palo Alto, CA (On-Site)/Full-time

About the role

Join our team to push the frontier of robot learning. You'll train general-purpose control policies at scale — spanning imitation learning and large multimodal models — build the data and evaluation pipelines that make them work, and take policies from training runs to real robots. We care more about your ability to train models that work than about any particular robot, task, or sensor you've used before.

Requirements

• 01MS or PhD in Robotics, Computer Science, Machine Learning, or related field—or equivalent experience
• 02Strong track record training neural networks end-to-end: you can take a model from idea to a working, debugged, reproducible result
• 03Experience developing robot learning policies like diffusion policies, 3D policies, vision-language-action models, or video action models
• 04Experience training models at scale: multi-GPU/multi-node training, large datasets, long runs, and debugging throughput, stability, and scaling behavior
• 05Strong software engineering skills in Python and PyTorch or JAX in Linux environments (C++ a plus)
• 06Experience building large-scale data pipelines for robot learning — demonstration collection, curation, filtering, and dataset design
• 07Comfortable training policies with high-dimensional action spaces and multimodal observations (vision, proprioception, language)
• 08Rigorous about evaluation: designing benchmarks, running ablations, and drawing correct conclusions from noisy real-world results
• 09(+) Hands-on robotics experience — hardware bring-up, teleoperation and real-world data collection, on-robot deployment
• 10(+) Experience with high-performance simulation (MuJoCo, Isaac Gym/Lab) and sim2real techniques (domain randomization, dynamics adaptation, residual policy learning)
• 11(+) Familiarity with contact-rich or dexterous manipulation, tactile sensing, or differentiable simulation

Details & responsibilities

• 01Design and implement scalable training pipelines for general-purpose manipulation policies
• 02Train large multimodal policies — diffusion, 3D, vision-language-action, and video action models — and push them to state-of-the-art performance on real tasks
• 03Build the data engine behind the models: demonstration collection, curation, filtering, and dataset design at scale
• 04Integrate visual, proprioceptive, and tactile feedback into policy architectures
• 05Take policies from training runs to real hardware, closing the loop with on-robot deployment and iteration
• 06Collaborate across AI, hardware, and perception teams to build closed-loop manipulation systems
• 07Publish or contribute to cutting-edge research while delivering production-quality control stacks

Compensation & benefits

• 01Competitive salary and meaningful equity
• 02Full health, dental, and vision insurance
• 03Access to custom-built dexterous robots
• 04Collaboration with leading researchers in robotics and AI
• 05Backed by YC and top-tier investors
• 06High-ownership role with the opportunity to lead core initiatives in real-world robot learning

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