Whoop2mo ago

Senior Machine Learning Engineer (Health)

United States·Bostonsenior

Data ScienceMachine Learning EngineerDataData & AI

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Quick Summary

Overview

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.

Technical Tools

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The Health team is responsible for developing novel algorithms and features that expand our health sensing capabilities. Our work spans several key areas, including women’s health, software as a medical device, 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 Machine Learning Engineer on our 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 engineering, and cloud infrastructure—deploying robust, scalable, and reliable ML solutions built on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and the ability to deliver production-ready ML services.

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 health insights and member impact.

Participate in on-call rotations for data science services, ensuring uptime and performance in production environments.

Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or a related field (Master’s preferred).

4+ years of professional experience as a Machine Learning Engineer or Software Engineer building production ML-enabled systems.

Proven experience working with time series data (wearable/physiological/high-frequency sensor data preferred).

Experience designing, deploying, and operating ML inference systems at scale (real-time streaming and/or large-scale batch).

Strong coding skills in Python with a track record of writing clean, well-tested, production-quality code.

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:Experience developing ML-enabled software in a regulated or quality-managed environment (e.g., QMS-controlled development for SaMD/medical devices), including documentation, traceability, validation/verification practices, and change control.

Preferred: Demonstrated technical leadership through architecture/design ownership, setting engineering standards, and raising quality via reviews and mentorship.

Preferred: Proven track record driving measurable improvements in system performance, reliability, and/or cost at scale, and influencing cross-functional technical direction.