Staff Applied Machine Learning Scientist, WHOOP
Health
$170-230k
+ Equity
Wearable health and fitness devices
Be an early applicant
Wearable health and fitness devices
1001+ employees
Be an early applicant
$170-230k
+ Equity
1001+ employees
Company mission
To unlock human performance and Healthspan.
Role
Who you are
- 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
- Strong Python skills with reproducible scientific / ML engineering hygiene (clean, reviewable code; comfort with stacks such as NumPy/SciPy/pandas, scikit-learn, PyTorch/TensorFlow)
- Clear, high-signal communicator who can move between deeply technical method discussions and cross-functional conversations with Product, clinical partners, and leadership
- Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply
What the job involves
- 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, clinical, and Digital Health partners on what the data can and cannot support
- Success in this role requires applied algorithm and statistical craft, the judgment to operate across Health problem types, and Staff-level leadership: launching domain work independently, raising the quality and velocity of the people around you, and making clear prioritization calls under uncertainty
- 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
- Set the methodological and quality standard for a Health domain: reusable evaluation practices, review expectations, and documentation that others can inherit
- Develop and refine approaches for time-series, longitudinal, and multimodal data, including data curation, labeling strategy, feature and representation choices, and offline evaluation that holds up under real-world noise, missingness, and population shift
- Partner with Machine Learning Engineers to translate validated methods into production-ready systems, making modeling choices that account for scalability, reliability, latency, cost, monitoring, and (where relevant) controlled-release constraints
- Serve as a technical counterpart to Product and Digital Health (clinical science, regulatory, quality): pressure-test performance requirements, study design, and evidence quality, and push back when a claim is not supportable
- Raise the quality and velocity of the scientists and engineers around you through design reviews, mentorship, and the judgment of when to coach someone else to champion a problem versus taking it yourself
- Communicate trade-offs, risks, and results clearly to technical, clinical, product, and leadership audiences; prevent misalignment early; drive decisions with evidence and a clear point of view on domain direction
Our take
WHOOP develops wearable health-tracking devices designed to help athletes optimise their performance. Its technology continuously monitors recovery, sleep, and training hours to fine-tune both individual and team performance. Unlike competitors that primarily track exercise data, WHOOP's holistic approach includes metrics like heart rate variability, which can influence more effective and healthier exercise routines.
By focusing on a subscription model that includes the cost of the physical device, WHOOP differentiates itself from competitors that charge a higher one-time fee for their products. This model provides ongoing value and encourages users to stay engaged with their health data.
WHOOP's comprehensive monitoring of strain and recovery has given it a competitive edge, even as other brands like Fitbit have incorporated similar features. Beyond sports performance, WHOOP has conducted extensive research on predicting premature births, aiming to integrate these findings into its app. This commitment to broader health applications demonstrates WHOOP's dedication to advancing wearable health technology.
Steph
Company Specialist at Welcome to the Jungle
Insights
1% employee growth in 12 months
Company
Funding (last 2 of 9 rounds)
Mar 2026
$575m
SERIES G
Aug 2021
$200m
SERIES F
Total funding: $979.8m
Company benefits
- Flexible Vacation: WHOOP has a flexible vacation policy that allows you to unwind, laptop-free, when you need it most.
- Focus on Family: We offer 18 weeks paid parental leave, plus an additional 2 weeks to gradually return to work.
- Get Invested: In addition to a competitive base salary and 401k, you're eligible to receive stock options to share in the future of WHOOP.
- WHOOP +1: Everyone on the team gets a complimentary WHOOP membership—plus another membership to share with a loved one.
- Health Matters: Our competitive benefits package covers medical, dental, and vision, in addition to mental health services, life and disability insurance, and more.
- Find Your Fit: Full-time employees get $500 a year to spend on wellness in whatever way they see fit, from fitness classes and memberships, to recovery services.
- It Pays to Sleep: Our Sleep Performance Program incentivizes employees to get more sleep. Log an average sleep performance of 85% or higher for the month and get a $100 bonus.
- Location, Location, Location: You can’t beat the views from the rooftop of One Kenmore Square. Feel inspired daily as you work, enjoy free snacks and coffee, and collaborate in the heart of Boston.
Company HQ
West Fens, Boston, MA
Leadership
Will Ahmed
(Founder & CEO)After graduating from Harvard with a Bachelor of Arts in Government and Economics, Will went on to hold Summer Analyst roles at Allen & Company and Lindsay Goldberg before founding WHOOP in 2012.
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