Staff Software Engineer Machine Learning (Health)

RESPONSIBILITIES

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.

 

RESPONSIBILITIES:

Design build and maintain production services that deliver health features in close collaboration with Applied ML Scientists and ML Research Engineers.
Lead the architecture and development of scalable ML inference systems APIs and backend services optimized for reliability latency and cost efficiency.
Collaborate with Data Platform teams to improve ML data pipelines tooling feature delivery systems and validation frameworks that support robust model performance.
Work alongside Applied ML Scientists to translate research prototypes into production systems that can be deployed monitored and operated at scale.
Partner with the Digital Health team on algorithmic performance specifications validation and verification planning and the design of SPA or algorithm validation studies.
Drive operational excellence through monitoring observability incident response and reliability improvements for ML-powered services.
Collaborate with researchers product teams and engineering stakeholders to align platform investments with health insights and member impact.
Participate in on-call rotations for ML and data services ensuring uptime performance and reliability in production environments.
Provide technical leadership through architecture reviews engineering standards mentorship and cross-functional collaboration.

QUALIFICATIONS:

Bachelor's degree in Computer Science Software Engineering Data Science Applied Mathematics or a related field (Master's preferred).
7+ years of professional experience as a Software Engineer Machine Learning Engineer Platform Engineer or related role building large-scale distributed systems and/or production ML platforms.
Strong coding skills in Python with a track record of writing clean well-tested production-quality code.
Strong fundamentals in backend and service development including APIs distributed systems reliability monitoring debugging and performance optimization.
Experience designing deploying and operating ML inference systems at scale (real-time streaming and/or large-scale batch).
Experience building and maintaining distributed systems event-driven architectures or high-throughput data processing platforms.
Experience deploying and operating services on cloud platforms (AWS or GCP) including Kubernetes CI/CD pipelines infrastructure automation and observability tooling.
Experience partnering with data science or machine learning teams to productionize models algorithms and data-driven features.
Familiarity with applied machine learning concepts model evaluation experimentation and performance validation.
Experience processing time-series streaming sensor wearable physiological or other high-volume data sources is preferred.
Experience developing software in a regulated or quality-managed environment (SaMD medical device healthcare fintech or similarly regulated domains) is a plus.
Demonstrated technical leadership through architecture and design ownership setting engineering standards and raising quality through reviews and mentorship.
Proven track record driving measurable improvements in system performance reliability scalability and/or cost at scale while influencing cross-functional technical direction.

What We Do

At WHOOP we’re on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable device and performance optimization platform has been adopted by many of the world's greatest athletes and consumers alike.

Why Work With Us

At WHOOP we’re focused on building an inclusive and equitable team with a strong sense of belonging for everyone—increasing representation in every way as our team grows. We believe that our differences are our source of strength—so much so it’s one of our core values.


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