Senior Machine Learning Engineer (Health)

Whoop • Boston, MA

102d

About The Position

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

Requirements

• Bachelor’s Degree in Computer Science, Data Science, Applied Mathematics, or a related field. Master’s preferred.
• 5+ years of professional experience as a Machine Learning Engineer or Software Engineer with focus on ML systems.
• Proven expertise working with time series data (wearable, physiological, or high-frequency sensor data strongly preferred).
• Experience designing and deploying ML inference systems at scale: both real-time streaming and large-scale batch pipelines.
• Strong coding skills in Python (scientific stack) and SQL, with a track record of writing clean, production-quality code.
• Strong communication skills to collaborate across engineering, research, and product teams.
• Proven experience deploying and maintaining ML systems on cloud platforms (AWS or GCP).
• Working familiarity with MLOps best practices: model versioning, CI/CD for ML, observability, and monitoring for inference systems.
• Ability to reason about and design for performance trade-offs (latency vs. throughput vs. cost) when building ML inference systems.
• Strong understanding of backend service development (APIs and service reliability) as it applies to serving ML models at scale.

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 health insights and member impact.
• Participate in on-call rotations for data science services, ensuring uptime and performance in production environments.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Bachelor's degree
• Number of Employees: 501-1,000 employees