Machine Learning Engineer

About Sprinter Health: At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year.   By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS . Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway.

We’re looking for an ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably. You sit between software engineering, data engineering, and modeling, and you make ML work in the real world and stay working. You will build training and inference pipelines, serve predictions through APIs and batch jobs, and stand up the monitoring that catches drift and silent degradation before they reach a patient or a partner. You will turn the models that scientists prototype into systems the company can depend on. The ideal candidate thinks in systems rather than notebooks, knows what a model needs to become production-ready, and builds clean interfaces between data, models, and product.   Hybrid & Office Experience We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work.

Production ML Systems Build and harden training pipelines. Package models for deployment. Serve predictions through APIs or batch jobs with reliability in mind. Maintain feature pipelines and keep features fresh and correct. Reliability & Observability Monitor drift, data quality, latency, cost, and performance. Automate retraining and validation, and design safe rollback. Prevent training-serving skew and silent model degradation. Collaboration & Craft Productionize models handed off from other teams. Build clean interfaces between data, model, and product systems. Implement reproducibility, versioning, and model-governance artifacts..   What you have done: Strong Python and software-engineering fundamentals. Experience with ML frameworks, data pipelines, and model serving. Experience taking models from prototype to reliable production. Cloud infrastructure, containers, CI/CD, and orchestration. Monitoring and observability, plus reproducibility and versioning across data, features, and models. Comfort with security and privacy controls for sensitive data.   What gives you an edge: Background in backend engineering, data engineering, MLOps, or platform engineering. Experience with feature stores or feature pipelines at scale. Familiarity with healthcare data and PHI-aware systems   Interview Process: We aim to complete the interview process between 2–3 weeks. It will usually consist of: Recruiter Screen (30 minutes) Hiring Manager Introduction (30 minutes) Hands-on-Keys Technical Assessment (1 hour) Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours) References   What we offer: Meaningful pre-IPO equity Medical, dental, and