Backed by SoftBank VF and IVP.
About the role
Lead the design, development, and validation of machine learning and statistical methods for Health features, using wearable physiological and behavioral data and related member sources. Frame ambiguous health and product questions into well-scoped modeling or study problems, with clear performance criteria, evaluation plans, and honest treatment of uncertainty and failure modes. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Master's or PhD (or equivalent professional experience) in Biomedical Engineering, Electrical Engineering, Computer Science, Applied Mathematics, Biostatistics, Bioinformatics, Physiology, or a related quantitative or health-science field
- 7+ years of professional experience developing algorithms, applied ML models, or statistically rigorous analyses in health, wearables, medical devices, digital health, or a closely related domain
- Demonstrated Staff-level technical leadership: setting methodological standards others reuse, influencing multi-team outcomes, mentoring senior ICs, and making prioritization calls under uncertainty. End-to-end project ownership alone is not sufficient
- Strong foundation in statistics (Bayesian and frequentist) and machine learning for time-series and longitudinal data, with the ability to draw defensible conclusions from observational, noisy, or otherwise messy real-world data
- Experience with wearable or physiological signals (PPG, IMU, HR/HRV, sleep, activity, temperature, or similar) or a closely related adjacent time-series domain
- Hands-on experience designing studies or evaluation protocols end-to-end: data curation, metric choice, validation splits, subgroup and failure analysis, and documentation of assumptions and limitations
More about this role
At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep.
The Health team develops the algorithms and features that expand WHOOP's health sensing capabilities. The work spans women's health, software as a medical device, wellness and longevity, member insights, and emerging health signals. We combine continuous physiological data with clinical research and domain expertise to ship features that are scientifically grounded and useful to millions of members.
As a Staff Applied Machine Learning Scientist on the Health team, you will turn ambiguous clinical and product questions into defensible studies, models, and evaluation plans on WHOOP's longitudinal wearable and member data (physiological signals, behavioral logs, labs and integrations, and related streams). You will set the methodological bar for a Health domain. You will partner with Machine Learning Engineers on the path to production, and with Product,...
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