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SeniorFull-timePythonAWSGCPMachine LearningData ScienceCI/CDML
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
WHOOP is seeking a Machine Learning Engineer to join our Health team, responsible for developing novel algorithms and features that expand our health sensing capabilities. You will design, build, and productionize ML systems that deliver personalized health metrics to millions of members.
Key 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
Requirements
- ✓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
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