$150,000 - 150,000 yearly
Join a small, fast-moving YC Spring 2026 team building the first wearable designed to read and train emotional intelligence. You'll work at the intersection of computational neuroscience and production machine learning, developing the core inference model that quantifies emotional states — energy, mood, and focus — from a rich stream of audio and biometric signals captured by a consumer wearable device.
This is a founding technical role with real ownership. As the company scales, you are expected to grow into a leadership position within the research and ML org. Prototypes are already on users' wrists, and the team is moving fast.
Design, develop, and iterate on the inference model that quantifies emotional states from wearable sensor data (150+ audio and biometric signals)
Bridge cutting-edge neuroscience research and production-grade machine learning — you will function effectively as both a research scientist and an ML engineer
Evaluate and deploy models into a real consumer product pipeline
Help define the scientific direction and methodology for emotional state quantification as the team and product scale
Required
PhD in computational neuroscience, cognitive science, active inference, or a closely related field — this is a hard requirement
Hands-on experience building and deploying inference models to quantify emotional or physiological states from wearable sensor data
Strong ML engineering skills: model development, evaluation, and deployment (Python stack)
Familiarity with active inference frameworks and computational neuroscience methods
Willingness to work on-site in San Francisco (or London)
Not a fit
Wet-lab-only neuroscience backgrounds without computational/ML experience
Salary: ~$150,000 USD (San Francisco) / ~£75,000 GBP (London)
Equity: Up to 1–2% (founding-level ownership)
Backed by angels and strategic partners (pre-Series A / seed stage)
Visa sponsorship: Not available — visa transfers are acceptable; otherwise the London office is an option
Primary: San Francisco, CA, USA (on-site preferred)
Alternative: London, UK (on-site)
Location is flexible between these two offices
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