As a Staff Software Engineer on our Clinical Health team, you will design, build, and operate the production systems that deliver personalized health insights to millions of WHOOP members. You will 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.

In this role, you will partner closely with Applied ML Scientists, ML Research Engineers, and Digital Health teams to translate novel algorithms and research prototypes into production-grade systems. You will 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.

This role is ideal for engineers with deep experience building distributed systems and production platforms who are excited to apply those skills to machine learning-powered healthcare products.

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.

The Health team is responsible for developing novel algorithms and features that expand our health sensing capabilities. Our work spans several key areas, including women's health, software as a medical device, wellness monitoring, longevity research, and emerging health insights. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members.

As a Staff Software Engineer, Machine Learning on our Clinical Health team, you will design, build, and operate the production systems that deliver meaningful, personalized health insights to millions of members. You will work at the intersection of software as a medical device (SaMD), machine learning, backend engineering, and cloud infrastructure—building scalable, reliable, and observable services that power health features derived from physiological and behavioral data streams.

A central part of this role is partnering with Applied ML Scientists, ML Research Engineers, and Digital Health teams to translate novel algorithms and research prototypes into production-grade systems. This role emphasizes distributed systems design, backend engineering excellence, platform thinking, and operational rigor. You will help define the architecture, tooling, and infrastructure required to deploy, validate, monitor, and operate ML-powered health features within a quality-managed framework.