Job description

At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives. The Health team develops the algorithms and features that expand WHOOP's health sensing capabilities. The work spans women's health, wellness and longevity, member insights, emerging health signals, and software as a medical device. Applied ML Scientists and ML Engineers work as a production unit: scientists own problem framing and modeling; engineers own the path to production. As a Senior Manager, Machine Learning on the Health Insights team, you will own delivery and people outcomes across a mixed team of scientists and engineers. You will translate department strategy into clear quarterly plans, develop senior ICs (and, as the team grows, the next layer of managers), hire against a high bar for both crafts, and partner with Product and adjacent Health partners on what is worth building and what the data can support. You will stay close enough to the ML lifecycle to evaluate the team's most important decisions without becoming the default contributor. Success in this role requires enough ML and health-domain fluency to earn trust from senior ICs, plus the people-leadership skill to grow that team, hold delivery across multiple workstreams, and make prioritization calls under uncertainty. RESPONSIBILITIES: - Own delivery, team health, and people outcomes across Health ML workstreams on a mixed Applied ML Scientist and ML Engineer team. - Translate department strategy into quarterly plans, milestones, and success criteria; keep the team on the highest-leverage work and adapt deliberately as priorities shift. - Build the team: lead hiring across science and engineering roles, calibrate the bar for each craft, and shape onboarding, leveling, and growth practices. - Develop the next layer of leadership: coach senior ICs (and managers, as the scope grows), manage performance with clarity and care, and create the conditions for people to operate above their level. - Set the standard for how Health ML gets built: work quality, evaluation rigor, review practices, and an operating model that holds as people and workstreams change. - Partner directly with Product (and with clinical, software, and Digital Health partners as the work requires) to align on roadmap, evidence, and sequencing; broker trade-offs between iteration speed, scientific integrity, and member value. - Own the risk posture for the team: anticipate execution, technical, and people risks, design mitigation into how the team operates, and keep senior leadership clearly informed of the most consequential decisions. - Define and own the operating model: planning cadences, design reviews, decision forums, and quality gates appropriate to member-facing health ML. - Build and continuously raise AI-enabled workflows that create measurable leverage across the team, for development, evaluation, documentation, and stakeholder communication. - Represent the team's work credibly to executive and cross-functional audiences with brevity, evidence, and clarity. QUALIFICATIONS: - 7+ years of experience in machine learning, applied science, or software engineering, with 3+ years managing engineering and/or science teams. - Bachelor's degree in Computer Science, Engineering, Applied Math, Biomedical Engineering, or a related field; advanced degree preferred. - Demonstrated track record managing highly senior ICs: hiring, performance management, and developing people who in turn raise the bar around them. - Deep familiarity with the ML development lifecycle (data collection, model training, evaluation, validation, deployment, and monitoring), sufficient to evaluate the most important technical and methodological decisions the team makes. - Experience shipping algorithms or ML-enabled product in health, wearables, digital health, or a closely related applied domain. - Demonstrated ability to set direction and make timely prioritization calls under uncertainty, keeping team focused on the highest-leverage work. - Demonstrated AI-tooling sophistication: a track record of building or adopting AI-enabled workflows that materially improved team speed, quality, or decision confidence. - Clear, high-signal communicator who can move fluidly between technical ML discussions and executive-level summaries of risk, trade-offs, and direction. - Depth in a Health domain WHOOP already works in preferred (member insights, women's health, cardiometabolic or respiratory health, sleep, strength / healthspan, neuroscience, or related physiological time-series work). - Preferred experience as a counterpart to Product on ambiguous health or member-insight problems: scoping, sequencing, and pushing back when a claim is not supportable. - Familiarity with clinical validation, evidence packages, or Digital Health partnership on higher-stakes health features preferred. 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. WHOOP is an Equal Opportunity Employer and participates in E-verify e-verify.gov to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values. At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success. The U.S. base salary range for this full-time position is $170,000 - $230,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements. Learn more about WHOOP whoop.com.

Requirements

Must-haves

At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.
Own delivery, team health, and people outcomes across Health ML workstreams on a mixed Applied ML Scientist and ML Engineer team.
Translate department strategy into quarterly plans, milestones, and success criteria; keep the team on the highest-leverage work and adapt deliberately as priorities shift.
Build the team: lead hiring across science and engineering roles, calibrate the bar for each craft, and shape onboarding, leveling, and growth practices.
Develop the next layer of leadership: coach senior ICs (and managers, as the scope grows), manage performance with clarity and care, and create the conditions for people to operate above their level.
Set the standard for how Health ML gets built: work quality, evaluation rigor, review practices, and an operating model that holds as people and workstreams change.
Own the risk posture for the team: anticipate execution, technical, and people risks, design mitigation into how the team operates, and keep senior leadership clearly informed of the most consequential decisions.
Define and own the operating model: planning cadences, design reviews, decision forums, and quality gates appropriate to member-facing health ML.
Build and continuously raise AI-enabled workflows that create measurable leverage across the team, for development, evaluation, documentation, and stakeholder communication.
Represent the team's work credibly to executive and cross-functional audiences with brevity, evidence, and clarity.
7+ years of experience in machine learning, applied science, or software engineering, with 3+ years managing engineering and/or science teams.
Demonstrated track record managing highly senior ICs: hiring, performance management, and developing people who in turn raise the bar around them.
Experience shipping algorithms or ML-enabled product in health, wearables, digital health, or a closely related applied domain.
Demonstrated ability to set direction and make timely prioritization calls under uncertainty, keeping team focused on the highest-leverage work.
Demonstrated AI-tooling sophistication: a track record of building or adopting AI-enabled workflows that materially improved team speed, quality, or decision confidence.
Clear, high-signal communicator who can move fluidly between technical ML discussions and executive-level summaries of risk, trade-offs, and direction.

Nice-to-haves

Bachelor's degree in Computer Science, Engineering, Applied Math, Biomedical Engineering, or a related field; advanced degree preferred.
Depth in a Health domain WHOOP already works in preferred (member insights, women's health, cardiometabolic or respiratory health, sleep, strength / healthspan, neuroscience, or related physiological time-series work).
Preferred experience as a counterpart to Product on ambiguous health or member-insight problems: scoping, sequencing, and pushing back when a claim is not supportable.
Familiarity with clinical validation, evidence packages, or Digital Health partnership on higher-stakes health features preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningai

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Wellness