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
- Preferred: Experience working with time series data (wearable, physiological, or high-frequency sensor data)
Skills Required
- Bachelor's Degree in Computer Science, Data Science, Applied Mathematics, or a related field
- 5+ years of professional experience as a Machine Learning Engineer or Software Engineer
- Proven expertise working with time series data
- Experience designing and deploying ML inference systems at scale
- Strong coding skills in Python and SQL
- Proven experience deploying and maintaining ML systems on cloud platforms
- Familiarity with MLOps best practices
- Strong understanding of backend service development
WHOOP Compensation & Benefits Highlights
-
Parental & Family Support — Policies include 18 weeks of paid parental leave plus two additional weeks to support return-to-work. Feedback suggests this depth of leave is a standout element of the package.
-
Wellbeing & Lifestyle Benefits — Offerings span medical/dental/vision, mental-health support, a $500 wellness stipend, daily meals, onsite gym/recovery tools, commuter benefits, and free WHOOP memberships. These wellness-forward perks collectively signal strong everyday support for employees’ health and lifestyle.
-
Equity Value & Accessibility — Stock options are granted at hire and ownership is emphasized as part of total rewards. Feedback suggests this equity component is valued alongside cash compensation.
WHOOP Insights
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.
Gallery
WHOOP Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.

.jpg)


.jpg)
