Founding Research Engineer, Robot Learning

About the Role

As the first robot learning hire at a physical AI data infrastructure company, you will train robot policies on synthetic data and rigorously measure its impact. You will work across research, engineering, and product teams to turn experimental results into data requirements, public demonstrations, and product direction.

What You'll Do

• Train robot policies using VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning in simulation, initially focusing on manipulation and grasping.

• Establish baselines on public evaluation suites, then run controlled comparisons to measure the effects of training data and report results, including failures.

• Translate experimental findings into written specifications for the data team, including guidance on data generation, fidelity, and variation.

• Publish and demonstrate results to the robotics community and participate in customer technical discussions.

• Begin as an individual contributor and build the Bay Area robotics team as it grows.

What We're Looking For

• At least 6 years of hands-on robot learning experience across industry and research environments, with 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 policy learning methods such as VLA fine-tuning, OpenVLA, π0, GR00T, imitation learning, diffusion policy, ACT, behavior cloning, or reinforcement learning.

• Experience with manipulation or grasping policies and controlled experiments using fixed seeds, checkpoints, and evaluation suites.

• Ability to use evaluation results to guide changes to training data or simulation for the next iteration.

• Working knowledge of Python, PyTorch, NVIDIA Isaac, USD, and sim-to-real transfer.

Compensation & Benefits

Annual salary range: $260,000 to $300,000 USD. Visa sponsorship is not available.

Location

Hybrid role based in San Francisco, United States.