Staff Software Engineer, Machine Learning (health)

Whoop Whoop · Consumer · Boston, MA · Machine Learning & Research

Staff Software Engineer on the Clinical Health team responsible for designing, building, and operating production systems that deliver personalized health insights using machine learning. The role involves working at the intersection of ML, backend engineering, cloud infrastructure, and SaMD, translating algorithms into production-grade systems and providing technical leadership for ML infrastructure, inference services, data pipelines, and platform architecture within a quality-managed environment.

What you'd actually do

  1. design, build, and operate the production systems that deliver personalized health insights to millions of WHOOP members
  2. work at the intersection of machine learning, backend engineering, cloud infrastructure, and software as a medical device (SaMD), building scalable, reliable, and observable services that power health features derived from physiological and behavioral data
  3. partner closely with Applied ML Scientists, ML Research Engineers, and Digital Health teams to translate novel algorithms and research prototypes into production-grade systems
  4. provide technical leadership across ML infrastructure, inference services, data pipelines, and platform architecture, ensuring our health algorithms can be deployed, monitored, validated, and operated at scale within a quality-managed environment

Skills

Required

  • design, build, and operate production systems
  • machine learning
  • backend engineering
  • cloud infrastructure
  • software as a medical device (SaMD)
  • scalable, reliable, and observable services
  • ML infrastructure
  • inference services
  • data pipelines
  • platform architecture
  • deployed, monitored, validated, and operated at scale
  • quality-managed environment
  • distributed systems
  • production platforms

Nice to have

  • translate novel algorithms and research prototypes into production-grade systems
  • partner closely with Applied ML Scientists, ML Research Engineers, and Digital Health teams

What the JD emphasized

  • software as a medical device (SaMD)
  • quality-managed environment
  • distributed systems
  • production platforms
  • machine learning-powered healthcare products

Other signals

  • production systems
  • personalized health insights
  • machine learning
  • backend engineering
  • cloud infrastructure
  • software as a medical device (SaMD)
  • scalable, reliable, and observable services
  • health algorithms
  • deployed, monitored, validated, and operated at scale
  • quality-managed environment
  • distributed systems
  • production platforms
  • machine learning-powered healthcare products