**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.
Visa sponsorship is offered for this role.
