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 turn raw, noisy biosignals into features and models that make our data useful for physical AI.
What you'll do
- Build feature extraction pipelines for EMG, tactile, and kinematic signals
- Time-align and fuse multimodal streams (video, touch, muscle activity)
- Develop models and representations of human manipulation and motor intent
- Define data quality metrics and help shape what we collect next
- Work closely with sensor and robotics teammates to close the loop from hardware to model
What we're looking for
- 2+ years working with time-series or biosignal data (EMG, EEG, IMU, neural recordings, or similar)
- Strong Python and ML fundamentals (PyTorch or similar)
- Solid signal processing skills: filtering, artifact removal, synchronization
- Experience with multimodal or self-supervised learning is a plus
- Background in neuroengineering, BME, robotics, or related fields is a plus
Why join
Early, in-person team in San Francisco. You'll own core technical decisions from day one.