About the role
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.
About
the Role 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. What you will do: Modeling & Optimization Turn ambiguous operational problems into well-posed optimization, forecasting, or simulation tasks. Build strong baselines and improve on them efficiently, adding complexity only when the value justifies it. Develop solutions across operations research, optimization, and machine learning, choosing the right tool for the problem. Run careful analysis and iterate toward decisions that improve real operational outcomes — cost per visit, clinician utilization, patient access, and visits completed. Evaluation & Scientific Rigor Design offline evaluations, simulated backtests, and live experiments that predict real-world operational impact. Find the gaps between a model’s assumptions and messy operational reality before they reach production. Choose metrics suited to stochastic, constrained, and partially observed operational systems. Interpret and communicate results effectively to cross-functional stakeholders. Collaboration & Delivery Partner with Engineering to productionize optimization and decision systems reliably. Work with operations partners and SMEs to validate assumptions and review where decisions break down. Explain tradeoffs, uncertainty, and limitations clearly to product and leadership. What you have done: Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation. Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end. Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow. Ability to turn an ambiguous problem into a well-posed optimization or forecasting task, discover and analyze related literature, and adapt/apply those methods to our tasks. Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data. Interest in operations collaboration and applied healthcare impact. What gives you an edge: 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. 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