# Senior Machine Learning Engineer, Health

**Company:** [WHOOP](https://hotfix.jobs/companies/whoop)
**Location:** Boston, MA
**Role:** ML Engineering
**Experience:** 7+ years
**Skills:** Python, Machine Learning, Time Series, AWS, GCP, APIs, CI/CD, MLOps, Data Pipelines
**Posted:** 2026-07-06

> Senior Machine Learning Engineer building and productionizing scalable ML systems for personalized health metrics from wearable physiological data. Requires 4+ years experience deploying inference at scale, strong Python and backend skills, and collaboration with data scientists on health features.

## Job Description

## 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).
- 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.
- Demonstrated technical leadership through architecture/design ownership, setting engineering standards, and raising quality via reviews and mentorship.
- Proven track record driving measurable improvements in system performance, reliability, and/or cost at scale, and influencing cross-functional technical direction.

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