Robot Learning Researcher​/Engineer (Imitation Learning, Foundation Models, RL

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

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.

• 01 MS or PhD in Robotics, Computer Science, Machine Learning, or related field-or equivalent experience
• 02 Strong track record training neural networks end-to-end: you can take a model from idea to a working, debugged, reproducible result
• 03 Experience developing robot learning policies like diffusion policies, 3D policies, vision-language-action models, or video action models
• 04 Experience training models at scale: multi-GPU/multi-node training, large datasets, long runs, and debugging throughput, stability, and scaling behavior
• 05 Strong software engineering skills in Python and PyTorch or JAX in Linux environments (C++ a plus)
• 06 Experience building large-scale data pipelines for robot learning - demonstration collection, curation, filtering, and dataset design
• 07 Comfortable training policies with high-dimensional action spaces and multimodal observations (vision, proprioception, language)
• 08 Rigorous 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 (Mu Jo Co , 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

• 01 Design and implement scalable training pipelines for general-purpose manipulation policies
• 02 Train large multimodal policies - diffusion, 3D, vision-language-action, and video action models - and push them to state-of-the-the advantage on real tasks
• 03 Build the data engine behind the models: demonstration collection, curation, filtering, and dataset design at scale
• 04 Integrate visual, proprioceptive, and tactile feedback into policy architectures
• 05 Take policies from training runs to real hardware, closing the loop with on-robot deployment and iteration
• 06 Collaborate across AI, hardware, and perception teams to build closed-loop manipulation systems
• 07 Publish or contribute to cutting-edge research while delivering production-quality control stacks
• 01 Competitive salary and meaningful equity
• 02 Full health, dental, and vision insurance
• 03 Access to custom-built dexterous robots
• 04 Collaboration with leading researchers in robotics and AI
• 05 Backed by YC and top-tier investors
• 06 High-ownership role with the opportunity to lead core initiatives in real-world robot learning