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Sprinter Health
San Francisco, California, United States
Source: Sprinter Health careers · View original posting
From Sprinter Health's posting. “We” and “our” refer to the employer.
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 Applied Scientist to turn Sprinter’s hardest logistics problems into optimization models and decision systems that get the right clinician to the right patient at the right time. Sprinter runs a two-sided operation — clinicians on one side, patients who need care at home on the other — and we must match supply to demand across large regions under complex constraints.
As an Applied Scientist, you will take ambiguous operational problems and shape them into well-posed tasks, strong baselines, and honest evaluations. The algorithms you build will answer questions like which clinician sees which patient, in what order, given drive time, appointment windows, and clinical constraints; how many clinicians to staff in each region next month; and how long a visit will take or whether a patient is likely to cancel.
This role sits at the intersection of research and engineering, blending scientific rigor with a deployment-oriented mindset. It also requires close cross-functional partnership with operations, product, and engineering stakeholders. The ideal candidate is a scientist-engineer who reasons from first principles about uncertainty and constraints, reaches for the simplest model that works, and can move from a formulation on the whiteboard to a decision that runs in production.
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.
MS or PhD in operations research, industrial engineering, computer science, applied math, statistics, machine learning, or a related quantitative field; exceptional applied experience can substitute.
Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting.
Experience shipping optimization or decision systems that reached production and had material real-world impact.
Hands-on experience with supply-and-demand matching in a marketplace, dispatch, or field-operations setting.
Fluency deciding when an exact optimization approach beats a heuristic or learned one, and vice versa.
We aim to complete the interview process between 2–3 weeks. It will usually consist of:
References
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