Join a physical AI data infrastructure company as its first robot learning hire. You will train and evaluate robot policies using synthetic data, establish evidence of what improves performance, and turn findings into both public results and data requirements.
Train robot policies using VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning in simulation, with an initial focus on manipulation and grasping.
Establish baselines on public evaluation suites and open policy architectures, then run controlled comparisons to measure the impact of synthetic training data.
Use consistent seeds, checkpoints, and evaluation protocols, and report results clearly, including unsuccessful experiments.
Translate findings into written data specifications, including requirements for fidelity and variation.
Publish and demonstrate results to the robotics community and take part in customer technical discussions.
Begin as an individual contributor and help hire and lead a growing Bay Area robotics team.
At least 6 years of hands-on robot learning experience, including work in industry and a university research lab.
A master's or PhD in robotics, machine learning, or a related field, with strong academic and research foundations.
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, imitation learning, diffusion policies, action chunking, or reinforcement learning.
Experience with manipulation or grasping policies, and the ability to use evaluation results to guide data or simulation changes for future training iterations.
Proficiency with Python and PyTorch; familiarity with USD and sim-to-real transfer is useful.
Experience designing controlled experiments that vary training data while keeping other evaluation conditions consistent.
Annual salary: $260,000 to $300,000. Visa sponsorship is not available.
Hybrid in San Francisco, California.
Job details are sourced from the employer's original posting.
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