Location: San Francisco, US or open to other US locations
About Us:
Every wearable on the market reports correlation and calls it insight. LifeOS is built to answer the harder question: what specifically changed your sleep, your recovery, your capacity today, and what should you do tomorrow. We build our own hardware so the signal is ours end to end, which means the ceiling on the science is set by us, not by a sensor vendor.
The founding team has built one of the largest global wearable companies, with 45M+ devices shipped. The hardware is not the hard part. The modelling and building the core intelligence is.
Responsibilities:
- The causal inference layer behind LifeOS: approach, validation methodology, and where we can and cannot make claims.
- The accuracy validation program across sensors and derived metrics, and the standard we publish against
- Turning our longitudinal dataset into defensible model quality and a compounding advantage
- Working with hardware on what we should be sensing next, because the science should pull the roadmap.
- Building the AI science team in San Francisco.
Requirements
- PhD or equivalent research depth in ML, causal inference, computational health, or a related field
- From a frontier lab, applied research group, or a health AI company where models shipped to real users
- Rigorous about what the data supports, and willing to say when it does not
- Interested in physiological signal, not just architecture
Not a fit if you want a pure research role with no product surface.