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Client Roles
ABOUT THE ROLE
Join a physical AI data and infrastructure company as its first robot learning hire. You will test whether synthetic training data improves robot policies, turning rigorous results into public demonstrations and actionable requirements for the data team. You will report to the VP of R&D and work with engineering and product colleagues across the Bay Area and Paris.
WHAT YOU'LL DO
- Train manipulation and grasping policies using synthetic data, including VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning in simulation.
- Establish baselines on public evaluation suites and open policy architectures, then run controlled comparisons that vary training data while keeping seeds and evaluation protocols consistent.
- Analyze and communicate results honestly, including cases where the data does not improve performance.
- Translate experimental findings into data specifications covering generation needs, fidelity, and variation.
- Publish and demonstrate results to the robotics community and participate in customer technical discussions.
- Start as an individual contributor and help hire and lead the Bay Area robotics team as it grows.
WHAT WE'RE LOOKING FOR
- Six or more years of hands-on robot learning experience, including experience in industry and a university research lab.
- A graduate degree in robotics, machine learning, or a related field.
- Experience training and evaluating robot policies in simulation using Isaac Sim, Isaac Lab, MuJoCo, or equivalent tools.
- Practical experience with methods such as