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Associate Director of Machine Learning (Core Algorithms)

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Onsite

Associate Director of Machine Learning (Core Algorithms)

WHOOP

Associate Director of Machine Learning (Core Algorithms)

Skills

Data ScienceMachine Learning

1 month ago

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Important information


Contract type:

Permanent

Salary:

$200,000 – $245,000 / year

Location:

Boston, MA, USA

Work mode:

Onsite

Published on:

11 September 2026

What they need


  • We are seeking an Associate Director, Core Algorithms (Cloud) to lead the teams responsible for WHOOP’s cloud-based algorithmic intelligence — the models and systems that transform physiological data into the sleep, recovery, and training insights our members rely on daily
  • This role is also responsible for ensuring our cloud algorithms evolve alongside WHOOP hardware, partnering with Sensor Intelligence and Hardware teams to translate new sensor capabilities into production-grade algorithmic experiences
  • You will own the technical vision, execution, and organizational health of this team. You’ll drive the evolution of our core production algorithms such as workout detection, strain, and sleep staging toward higher accuracy, better member experiences, and more mature development practices
  • You will partner closely with ML Platform, Sensor Intelligence, Research, Product, and Software Engineering to define what WHOOP algorithms can enable — not just how they perform technically, but how they show up for members
  • Lead the cloud ML team responsible for the algorithms powering sleep, recovery, and training
  • Directly manage applied ML scientists and ML engineers; provide coaching, career development, and performance feedback that grows individual contributors into strong technical leaders
  • Ensure the technical quality bar for algorithm development is maintained by establishing the processes, reviews, and standards that guarantee rigor from research through deployment, and diving into designs and architectural decisions where necessary
  • Help drive the vision for what WHOOP algorithms and next-generation sensors can enable; advocate for member experience and push the boundaries of what our data makes possible
  • Ensure cloud algorithms remain compatible with future hardware generations; partner with Sensor Intelligence and Hardware to evolve proof-of-concept algorithms that leverage new sensor capabilities and bring them to production readiness
  • Establish and improve development lifecycle practices: experiment management, model validation, deployment pipelines, and production monitoring
  • Partner with ML Platform / MLOps to define requirements and drive maturity improvements across experiment tracking, model monitoring, deployment automation, and observability
  • Drive cross-functional alignment with Sensor Intelligence, Product, Software Engineering, and Research teams

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.- This is a role for someone who has built and shipped physiological ML at scale in a consumer product, who has a deep product instinct for what algorithms mean to end users, and who knows how to elevate an ML organization’s practices: raising the bar on tooling, processes, and standards to match the ambition of the work
  • Strong communication skills with the ability to translate complex ML concepts to diverse audiences including product, engineering, and executive stakeholders
  • Ability to evaluate technical designs, guide architectural decisions, and ensure quality at the system level, without needing to write code day-to-day
  • 8+ years of experience in machine learning or applied data science, with hands-on experience developing and shipping ML models for a consumer product
  • 4+ years of people leadership experience directly managing machine learning scientists/engineers, with demonstrated growth of team members and a track record of building high-performing teams
  • Experience scaling a production ML organization: growing teams and leaders, identifying gaps in the development lifecycle, and driving improvements that increase velocity, reliability, and rigor
  • Deep product sense: ability to think about algorithms from the member’s perspective, drive the vision for what algorithms can enable, and ensure the team is building toward meaningful user outcomes
  • Experience defining and driving cross-functional programs with engineering, product, and science partners
  • 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
  • Experience building algorithms using physiological or wearable sensor data (e.g., PPG, accelerometer, temperature, bioimpedance)
  • Experience managing through hardware-coupled development timelines where sensor availability and device generations constrain algorithm roadmaps
  • Familiarity with time-series modeling, sequential data, and the specific challenges of continuous physiological monitoring

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