About the Role
As the first robot learning hire, you will bring policy training and evaluation in-house at a physical AI data and simulation company. Working with research, engineering, and product teams, you will establish whether synthetic training data improves robot performance and turn findings into product requirements.
What You'll Do
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Train robot policies using synthetic data, including VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning in simulation.
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Establish baselines on public evaluation suites and run controlled comparisons to measure the impact of training data on manipulation and grasping.
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Document results honestly, including unsuccessful experiments, and use evaluation findings to guide subsequent data and simulation changes.
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Translate experimental findings into written specifications for data generation, fidelity, and variation.
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Publish and demonstrate results to the robotics community and participate in customer technical discussions.
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Help build and lead the robotics team as it grows.
What We're Looking For
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At least 6 years of hands-on robot learning experience, including work in industry and a university research lab.
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A master's or doctoral degree in robotics, machine learning, or a related field.
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Experience training and evaluating robot policies in simulation with Isaac Sim, Isaac Lab, MuJoCo, or equivalent tools.
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Practical experience with policy learning methods such as VLA fine-tuning, imitation learning, diffusion policies, ACT, behavior cloning, or reinforcement learning.
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Experience with manipulation or grasping policies; navigation policy experience is also relevant.
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Ability to run controlled experiments with fixed seeds, checkpoints, and evaluation suites, changing only the training data.
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Experience using evaluation results to guide data or simulation changes for subsequent training iterations.
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Relevant tools include Python, PyTorch, NVIDIA Isaac, and USD.
Compensation & Benefits
Base salary: $260,000 to $300,000 USD annually. Visa sponsorship is not available.
Location
Hybrid role based in San Francisco, United States.
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