Whoop

Whoop

Senior Product Manager, AI

Boston, MA · Senior · Full-time

Sponsorship not specified$155k-$215kDetected 30 days ago
SQLRESTMachine LearningResearch

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odds of building a lasting career here

55Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role66
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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H-1B wage level

the lottery is wage-weighted — each level is one more entry

Level IV · 4×
Level I$71,8431 entry
Level II$99,0502 entries
Level III$126,2773 entries
Level IV$153,4834 entries

This range already reaches Level IV — the maximum four lottery entries.

DOL prevailing wage, 2026-27 wage year · Computer Occupations, All Other (15-1299) · Boston-Cambridge-Newton, MA-NH. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.

Employer immigration record

from this employer's Department of Labor filings

Files H-1B transfers

10 transfer filings in the last year, covering 10 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • The AI team at WHOOP turns continuous physiological data into recommendations members act on every day.
  • WHOOP is hiring a Senior Product Manager to work at that layer, alongside our AI engineers, research scientists, and data scientists.
  • Your users are as often internal (agent authors, analysts, engineers, research scientists) as they are members.

Responsibilities

  • Build the evaluation habit across the company: make evals self-serve, and get the teams shipping AI features to actually run them.
  • Product-manage our internal AI development platform and drive its adoption, so that anyone at WHOOP building with AI has a fast, well-instrumented path from idea to shipped.
  • Track the unit economics of AI at WHOOP, including cost per interaction, and drive the tradeoffs between quality, latency, and spend.
  • WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
  • The quality of that intelligence is the product, and as it reaches more members across more surfaces, the work of measuring it, choosing the models behind it, understanding what it costs, and giving the rest of the company the tooling to build on it has grown into a role of its own.
  • The work spans how AI output quality gets measured and enforced, which models and providers power which experiences, the internal AI platform that teams across WHOOP use to build agents, and which emerging techniques from the research world are worth a bet.

Requirements

  • Depth in evaluation and measurement: you have defined how something gets measured, not only reported on it.
  • Experience with platform or internal-facing products, where the users are other teams.
  • A track record of driving cross-team programs with many dependencies and no direct authority.
  • Sound business judgment: you can quantify a tradeoff, reason about cost, and decline work that will not pay for itself.
  • Comfort operating in ambiguity, with evidence of turning a broad set of possibilities into one well-scoped bet.
  • 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.
  • Experience shipping AI or machine learning products, where the underlying capability was probabilistic rather than deterministic.
  • Working literacy in modern ML: you understand what fine-tuning is and the main flavors of it, what RL-based post-training is for, and how the current generation of techniques fits together. You do not need to implement them, but you should know what they buy you and what they cost.
  • Technical fluency sufficient to partner with research and engineering as a peer, and to translate model behavior into member experience.
  • Nice to have: internal AI tooling or evaluation frameworks
  • This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.

Nice to have

  • Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.
  • internal AI tooling or evaluation frameworks
  • experience partnering with a research organization
  • wearables or sensor data
  • SQL or light scripting.
  • This role is based in the WHOOP office located in Boston, MA.
  • The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
  • Interested in the role, but don't meet every qualification?

Compensation

  • WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/to determine employment eligibilityThe WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering co

Company info

  • make evals self-serve, and get the teams shipping AI features to actually run them.
  • Help decide which models power which experiences, and which providers we run them on, balancing quality, latency, and cost as the model landscape shifts.
  • Explore where AI capability goes next, from fine-tuning and reinforcement learning to distillation and emerging foundation models, and turn a broad field of options into a small number of well-scoped bets, starting with internal productivity.
  • At WHOOP, we're on a mission to unlock human performance and healthspan.
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This listing is sourced directly from Whoop's careers page and normalized into a canonical job model.