Senior Machine Learning Engineer, Insights

Posted Yesterday
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Boston, MA, USA
Hybrid
Senior level
Fitness • Hardware • Healthtech • Sports • Wearables
Power your performance with 24/7 data
The Role
Develop and deploy scalable ML systems for health metrics from physiological data. Collaborate with data scientists, MLOps, and engineers to productionize models, improve data pipelines, ensure reliability, and support on-call operations to maintain uptime and performance.
Summary Generated by Built In

WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. 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 Insights team is responsible for developing novel algorithms and features that expand our health capabilities. Our work spans several key areas, including women’s health, medical device-grade metrics, wellness monitoring, longevity research, and emerging health insights. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members.

As a Senior Machine Learning Engineer on our Health Insights team, you will help develop and deploy machine learning systems that deliver meaningful, personalized health metrics to millions of members. You will work at the intersection of data science, backend engineering, and health research, contributing to scalable ML solutions built on physiological and behavioral data streams. This role emphasizes robust system design, performance, and reliability in production.

RESPONSIBILITIES:

  • Create, improve, and maintain production services that provide analysis for health features in collaboration with data scientists and MLOps engineers
  • Collaborate with data engineers to improve ML data pipelines, tooling, and validation systems that support robust model performance
  • Work alongside data scientists to translate research prototypes into production ML systems optimized for scale, latency and cost efficiency
  • Collaborate with researchers and product teams to align model development with physiological insights and member impact
  • Participate in on-call rotations for data science services, ensuring uptime and performance in production environments

QUALIFICATIONS

  • Bachelor's Degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master’s preferred). 
  • 4+ years of professional experience as a ML engineer, applied researcher, or software engineer with a focus on ML systems
  • Strong coding skills in Python with a track record of writing clean, production-quality code
  • Experience designing, deploying and operating ML inference systems at scale (real-time streaming and/or large-scale batch)
  • Strong fundamentals in backend/service development (APIs, reliability, monitoring, debugging) as it relates to serving ML models
  • Experience deploying and maintaining ML systems on cloud platforms (AWS or GCP), including CI/CD and observability practices
  • Familiarity with applied ML development (frameworks, evaluation criteria, performance validation) and translating prototypes into production systems
  • Preferred: 2+ years of experience applying advanced mathematical and statistical techniques
  • Preferred: Experience working with time series data (wearable, physiological, or high-frequency sensor data) 

Skills Required

  • Bachelor's degree in Computer Science, Data Science, Applied Mathematics, or related field
  • 4+ years professional experience as an ML engineer, applied researcher, or software engineer focused on ML systems
  • Strong coding skills in Python and production-quality code experience
  • Experience designing, deploying, and operating ML inference systems at scale (real-time streaming and/or large-scale batch)
  • Strong backend/service development fundamentals (APIs, reliability, monitoring, debugging) related to serving ML models
  • Experience deploying and maintaining ML systems on cloud platforms (AWS or GCP), including CI/CD and observability practices
  • Familiarity with applied ML development: frameworks, evaluation criteria, and performance validation
  • Master's degree in a related field
  • 2+ years applying advanced mathematical and statistical techniques
  • Experience working with time series data (wearable, physiological, or high-frequency sensor data)

What the Team is Saying

Josh
Manan Dedhia
Anahis

WHOOP Compensation & Benefits Highlights

  • Parental & Family Support Parental leave is described as generous, with an additional transition period to support return-to-work. Feedback suggests this policy stands out within the overall package.
  • Wellbeing & Lifestyle Benefits Wellness stipends, free WHOOP memberships (including a giftable one), a sleep-performance cash incentive, daily meals, and access to onsite gym and recovery tools create a lifestyle-oriented offering. Feedback suggests these perks meaningfully enhance the total rewards experience beyond base pay.
  • Equity Value & Accessibility Equity participation and stock option eligibility are highlighted, and ownership is portrayed as rewarding as the company grows. Feedback suggests this sense of ownership positively influences perceptions of compensation.

WHOOP Insights

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The Company
HQ: Boston, MA
500 Employees
Year Founded: 2012

What We Do

At WHOOP, we’re on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable device and performance optimization platform has been adopted by many of the world's greatest athletes and consumers alike.

Why Work With Us

At WHOOP, we’re focused on building an inclusive and equitable team with a strong sense of belonging for everyone—increasing representation in every way as our team grows. We believe that our differences are our source of strength—so much so it’s one of our core values.


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WHOOP Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Typical time on-site: 4 days a week
HQBoston, MA
Limerick, Limerick, V94 4D83 Ireland
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