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 data science algorithms teams are responsible for developing novel algorithms and features that expand our health and fitness capabilities with medical-grade metrics. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members. Currently, we have two Senior MLE roles open across two teams:
DS Health team: novel algorithms within the domains of women's health, multimodal longitudinal health insights, and SaMD
DS Core Algos team: performance-related insights for sleep, recovery, or exercise
As a Senior Machine Learning Engineer on our Core Algos or 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 engineer, and cloud infrastructure – deploying robust, scalable, and reliable ML solutions build on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and ability to deliver production-ready ML systems.
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 physiological 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 ML engineer, applied researcher, or software engineer with a focus on ML systems
Strong coding skills in Python with a track record of writing clean, production-quality code
Experience designing, deploying and operating ML inference systems at scale (real-time streaming and/or large-scale batch)
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: 2+ years of experience applying advanced mathematical and statistical techniques
Preferred: Experience working with time series data (wearable, physiological, or high-frequency sensor data)
