Founding Research Engineer, Robot Learning

About the Role

A physical AI data infrastructure company is seeking its first robot learning hire to test how synthetic training data affects robot policy performance. You will work with research, engineering, and product teams to turn rigorous experiments into public results and practical data requirements.

What You'll Do

• Train robot policies using synthetic data, including VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning in simulation.

• Establish baselines on public evaluation suites and compare results using controlled experiments with consistent seeds and protocols.

• Assess manipulation and grasping performance, reporting improvements and failures clearly.

• Translate experimental findings into data specifications covering generation needs, fidelity, and variation.

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

• Help build and lead a robotics team as it grows.

What We're Looking For

• At least 6 years of hands-on robot learning experience, including work in industry and university research environments.

• A master's or doctoral degree in robotics, machine learning, or a related field.

• Experience training and evaluating robot policies in simulation with tools such as NVIDIA Isaac Sim or Isaac Lab, MuJoCo, or equivalent.

• Experience with methods such as VLA fine-tuning, imitation learning, diffusion policy, ACT, behavior cloning, or reinforcement learning.

• Background in robotics research or simulation products, with experience training manipulation or grasping policies.

• Ability to use evaluation results to guide data or simulation changes and run controlled comparisons that vary training data while keeping other factors consistent.

• Strong Python and PyTorch skills; familiarity with USD and sim-to-real transfer is relevant.

Compensation & Benefits

Base salary: $260,000 to $300,000 annually. Visa sponsorship is not available.

Location

Hybrid role based in San Francisco, United States.