Machine Learning Engineer (Staff)

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.

About the Role

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.

Office Location

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.

What you will do

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

Implement monitoring for model performance, drift, data quality, latency, cost, reliability, and production behavior

Prevent training-serving skew, silent degradation, and model regressions before they become production issues

Automate retraining, validation, deployment, rollback, and other production ML workflows where appropriate

Establish reproducibility, versioning, model governance, and operational readiness practices as company defaults

Partner with engineering, data platform, product, operations, and applied science teams to productionize models and improve handoffs

Write design docs, define technical standards, and bring the broader engineering organization along on key ML infrastructure decisions

Set the technical bar for ML engineering by helping interview, mentor, and eventually hire engineers who follow

What you have done

Spent 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems

Built and owned ML systems in production across training, serving, features, monitoring, and deployment

Taken models from prototype or research stage into reliable, production-grade systems

Built or meaningfully scaled ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure

Designed systems that other engineers, data scientists, analysts, or product teams rely on

Made architectural decisions around ML platform design, serving patterns, feature infrastructure, build versus buy, and operational standards

Worked with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows

Built monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services

Created reproducible workflows across data, features, models, training runs, deployments, or experiments

Partnered closely with data science, applied science, data platform, product, operations, or backend engineering teams

Operated in ambiguous environments where there was no existing playbook and technical decisions had a long half-life

Balanced speed, simplicity, reliability, privacy, and long-term maintainability in production systems

What gives you an edge

You’ve been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup

You’ve built ML infrastructure in a high-growth or operationally complex environment

You have depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems

You have a background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering

You have experience with feature stores, feature pipelines, or production data systems at scale

You’ve helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers

You’ve worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments

You have experience with security, privacy, governance, or compliance considerations for production ML systems

What makes you successful

You decide what the pattern should be and bring the rest of the organization along

You reach for the simplest system that works, adding complexity only when the value justifies it

You know what it takes to make a model production-ready and can communicate those requirements clearly

You are an accelerator for applied science, data, product, and engineering teams, not a gatekeeper

You build interfaces that make models easy to consume and hard to misuse

You prevent silent degradation before it becomes an incident

You create standards that help future e