Staff Machine Learning Engineer ($220K - $270K + Equity) at Sprinter Health

Job Title Staff Machine Learning Engineer Salary $220K — $270K + Equity Company Description Sprinter Health is a $125M venture-backed healthcare technology company reimagining how people access care by bringing it directly to their homes. Backed by top-tier investors including a16z, General Catalyst, GV, and Accel, they have already supported over 2 million patients across 22 states while maintaining an exceptional 92 NPS. Job Description As the first dedicated ML Engineering hire, you will build the production systems that train, deploy, and monitor machine learning models company-wide. You’ll define Sprinter Health’s MLOps blueprint—from inference pipelines to model governance—transforming research prototypes into reliable, scalable systems that directly improve patient outcomes and optimize complex last-mile clinical operations. Location San Francisco, USA Why this role is remarkable Join as the first-of-function ML engineer, making foundational “build vs. buy” decisions and setting the technical architecture for all future hires. Work for a mission-driven company with $125M in funding, multi-year runway, and a proven track record of serving over 2 million patients. Enjoy a high-collaboration culture with daily team lunches, hybrid flexibility, and comprehensive benefits including 100% employer-paid healthcare premiums for you and dependents. What You Will Do Build and lead the ML engineering function, designing the foundational infrastructure for model training, real-time serving, and feature pipelines. Implement robust monitoring and observability systems to detect drift, data quality issues, and performance regressions before they impact clinical operations. Partner with product and applied science teams to package models into reliable APIs and batch jobs that power Sprinter Health’s core marketplace. The ideal candidate Brings 8+ years of experience in production software and ML systems, with a track record of scaling infrastructure from the ground up. Possesses deep expertise in cloud infrastructure, containerization, and MLOps paradigms across training, serving, and model governance. Thrives in ambiguous startup environments, balancing architectural longevity with the need for speed and simplicity in a high-growth setting. #J-18808-Ljbffr Jack & Jill