Machine Learning Engineer

Staff 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 has raised over $125M from investors like a16z, General Catalyst, GV, and Accel and enjoys multi-year runway.

We’re looking for a Staff Machine Learning Engineer to be Sprinter’s first dedicated ML engineering hire and build the production systems that train, deploy, monitor, retrain, and serve machine learning models across the company.

This is a founding, first-of-function role. You will define the blueprint for how ML moves from prototype to production at Sprinter, including our training and inference pipelines, serving patterns, feature workflows, monitoring, validation, retraining, and model governance practices.

You’ll work closely with engineering, data, product, operations, and applied science teams to turn models into reliable systems the company can depend on. That includes serving predictions through APIs and batch jobs, building clean interfaces between data and product systems, and implementing the observability needed to catch drift, data quality issues, latency problems, cost regressions, and silent model degradation before they impact patients or operations.

Just as importantly, you’ll make the foundational calls that every future model and ML engineer will build on: build versus buy, serving architecture, feature paradigms, deployment standards, monitoring expectations, and the guardrails that allow us to move quickly without creating fragile systems.

This role is ideal for a staff-level, hands‑on engineer who thinks in systems, has built ML infrastructure from the ground up, and knows how to right‑size solutions for a rapidly growing startup. You should be someone who empowers the teams around you, accelerates time to deployment, and knows what a model needs to be truly production-ready.

As the function grows, you will have the opportunity to shape the team, define the technical bar, and help build the ML engineering foundation for Sprinter.

We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. 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.

• Build and lead Sprinter’s ML engineering function as the company’s first dedicated ML engineering hire

• Define Sprinter’s ML platform and deployment paradigm across training, serving, features, monitoring, retraining, and governance

• Make foundational build‑versus‑buy, architecture, tooling, and platform decisions that future models and engineers will build on

• Design and build production training and inference pipelines that are reliable, observable, and maintainable

• Package models for deployment and serve predictions through APIs, batch jobs, or other production workflows

• Build clean interfaces between data systems, models, and product systems so ML can be consumed safely and reliably

• Maintain feature pipelines and ensure features remain fresh, correct, and consistent between training and serving

• Imple…