Sprinter Health combines technology and a full-stack medical practice to reimagine care at home. Backed by Accel, General Catalyst and a16z.
About the role
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.
What they're looking for
- 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
- Strong Python and software-engineering fundamentals
- Experience with ML frameworks, data pipelines, and model serving
- Experience taking models from prototype to reliable production
More about this role
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.
About the Role
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,...
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