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This listing was closed on Jul 21, 2026.
Browse Open JobsStaff Software Engineer, Machine Learning (Health)
Summary by AIEngineer.careers
Staff Software Engineer, Machine Learning (Health) is a Machine Learning Engineer role (full-time). with WHOOP. in BOSTON, US. Compensation shown: $205K–$285K. Imported listing (source: sportstechjobs.com). Apply on the employer's site (sportstechjobs.com).
Imported listing (source: sportstechjobs.com) · Apply on sportstechjobs.com
BOSTON · US
$205K–$285K
Full-time
STAFF
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from sportstechjobs.com
WHOOP seeks a Staff Software Engineer to design, build, and operate production ML systems delivering personalized health insights. This role involves collaborating with ML scientists to deploy algorithms at scale, building inference services, and ensuring platform reliability. It requires deep experience in distributed systems and production ML platforms.
Responsibilities
- Design and maintain production services for health features
- Lead architecture of scalable ML inference systems, APIs, and backend services
- Collaborate with data platform teams to improve ML data pipelines and validation frameworks
- Translate research prototypes into production systems for deployment and monitoring
- Drive operational excellence through monitoring, observability, and incident response
- Participate in on-call rotations for ML and data services
Requirements
- Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field (Master's preferred)
- 7+ years experience as Software Engineer, ML Engineer, or related role building large-scale distributed systems or production ML platforms
- Strong coding skills in Python with production-quality code
WHOOP
Boston · US · 1175+ employees
WHOOP provides a wearable fitness tracker that monitors biometric data to deliver personalized health and performance insights. It is widely used by athletes for recovery and sleep tracking.
