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.