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 Core Algorithms team is responsible for developing the machine learning algorithms that power WHOOP's core performance insights across sleep, recovery, and exercise. By combining continuous physiological and behavioral data with cutting-edge machine learning techniques, scientific research, and domain expertise, we build scalable, production-ready algorithms that deliver accurate, personalized insights to millions of members. From advancing existing models to developing entirely new algorithms, the team plays a critical role in translating complex data into meaningful health and performance metrics.
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)
