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The Role
As a Senior Machine Learning Engineer in the Health team, you'll design and productionize ML systems for personalized health metrics, collaborating with data scientists and engineers to optimize performance and ensure service reliability.
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.
Our data science algorithms teams are responsible for developing novel algorithms and features that expand our health and fitness capabilities with medical-grade metrics. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members. Currently, we have two Senior MLE roles open across two teams:
DS Health team: novel algorithms within the domains of women's health, multimodal longitudinal health insights, and SaMD
DS Core Algos team: performance-related insights for sleep, recovery, or exercise
As a Senior Machine Learning Engineer on our Core Algos or Health team, you will design, build, and productionize ML systems that deliver meaningful, personalized health metrics to millions of members. You will work at the intersection of data science, backend engineer, and cloud infrastructure – deploying robust, scalable, and reliable ML solutions build on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and ability to deliver production-ready ML systems.
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)
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? 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 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.
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 $150,000-$210,000. 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.
Top Skills
AWS
GCP
Python
SQL
What the Team is Saying
Josh
Lead Designer
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Field Quality Manager
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The Company
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
