Staff Machine Learning Engineer (Health)

United States·Bostonlead

OtherStaff Machine Learning Engineer

0 views0 saves0 applied

Apply Now

Quick Summary

Overview

WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance.

Technical Tools

awsgcppythonci-cdetlhealthtechmachine-learningmentoringsystem-design

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 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 Staff Machine Learning Engineer on our Clinical Health team, you will design, build, and productionize ML systems that deliver meaningful, personalized health insights to millions of members. You will work at the intersection of software as a medical device (SaMD), machine learning, backend engineering, and cloud infrastructure—deploying robust, scalable, and reliable ML solutions built on physiological and behavioral data streams. A central part of this role is collaborating with crossfunctional teams that own regulatory, quality, and clinical strategy, to ensure our algorithms are developed with the rigor required for regulated software. This role emphasizes strong coding skills, system design, and the ability to deliver production-ready ML services within a quality-managed framework.

Design, build, and maintain production services that deliver health features, in close collaboration with Applied ML Scientists and ML Research Engineers.

Collaborate with Data Platform teams to improve ML data pipelines, tooling, and validation systems that support robust model performance.

Work alongside Applied ML Scientists to translate research prototypes into production ML systems optimized for scale, latency, and cost efficiency.

Partner with the Digital Health team on algorithmic performance specifications, validation and verification planning, and the design of SPA or algorithm validation studies.

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). 7+ years of professional experience as a Machine Learning Engineer or Software Engineer building production ML systems.

Proven experience working with time series data (wearable, physiological, or 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.

Experience developing ML-enabled software in a regulated or quality-managed environment (SaMD or medical device), with working knowledge of change control, quality documentation, traceability, and verification/validation practices.

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

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