Robotics Scientist (Policy Learning)

About Preload

Preload builds the data layer for physical AI. Robots learn mostly from video, but human interaction is much richer: touch, force, and muscle activity. We build a wearable multimodal stack (egocentric video, tactile gloves, EMG) to capture how people actually interact with the physical world, and turn it into datasets robotics teams can train on. We're a YC (F26) team of PhD founders from Johns Hopkins and Caltech with backgrounds at Neuralink, Paradromics, and other neurotech companies.

The role

You'll prove our data makes robots better. You'll train and deploy manipulation policies on our sensorimotor datasets and benchmark the gains against video-only baselines.

What you'll do

• Train manipulation policies (imitation learning, VLAs, RL) using our multimodal human data
• Deploy and evaluate policies on real robot hardware
• Design benchmarks that measure how tactile and EMG data improve performance
• Feed results back to shape what we collect and how we process it
• Help customers and partners get value from our datasets

What we're looking for

• 2+ years training and deploying robot learning policies (industry or research)
• Strong Python and PyTorch; experience with ROS or similar robotics stacks
• Hands-on experience with real robot manipulation, not only simulation
• Experience with tactile sensing, dexterous hands, or learning from human demonstrations is a plus
• PhD or MS in robotics, ML, or related field is a plus

Why join

Early, in-person team in San Francisco. Your benchmarks will be the core proof point of the company.