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WHOOPWHOOPBoston, MA

Senior Machine Learning Engineer (Data Science Algorithms)

Designs, builds, and productionizes scalable ML systems using physiological data to deliver personalized health metrics. Requires 4+ years ML engineering experience, strong Python, cloud deployment, and backend skills.

Salary not listed
On-site4+ YOEML Engineering

About the role

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

Skills

PythonMachine LearningAWSGCPCI/CDMl PipelinesBackend DevelopmentAPIsTime SeriesMLOps

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