Senior Machine Learning Engineer (Health)

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.

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.

QUALIFICATIONS:

• 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.

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.