Senior Machine Learning Engineer, Core Algorithms

About the role

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.

Our Machine Learning Core Algorithms team is responsible for developing novel algorithms and features that expand our health and fitness capabilities with wearable sensor data. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members. This role is located on our Core Algorithms team: performance-related insights for sleep, recovery, and exercise.

As a Senior Machine Learning Engineer on our Core Algorithms team, you will design, build, and productionize ML systems that deliver meaningful, personalized metrics to millions of members. You will own the ML systems you build in collaboration with applied machine learning scientists. 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 systems.

Responsibilities:

- Create, improve, and maintain production services that provide analysis for core features in collaboration with applied ML scientists and MLOps engineers.

- 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.

- Collaborate with researchers and Product teams to align model development with physiological insights and member impact.

- Build operational maturity within the Core Algorithms team as well as the broader Machine Learning team, developing processes for observability, alerting, and incident response (including on-call rotations).

- Develop applied ML operational infrastructure (frameworks, evaluation criteria, performance validation).

- Mentor other engineers and applied ML scientists on production practices, raising the bar through code and system design review.

Qualifications:

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

- 4+ years of professional experience as an ML engineer, applied researcher, or software engineer with a focus on ML systems.

- Experience working with time series data (wearable, physiological, or high-frequency sensor data).

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

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