Backed by SoftBank VF and IVP.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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 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.
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