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 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 an AI Enablement Engineer to help every team at Sprinter build, adopt, and safely scale AI-powered workflows. This role is about turning AI from a set of tools into a company-wide operating advantage. You’ll work across engineering, operations, clinical, data, finance, and other teams to understand how work actually gets done, identify high-leverage opportunities for AI, and turn those opportunities into practical systems people can use. You’ll build bespoke agents, internal workflows, reusable templates, prompt and skill libraries, evaluation frameworks, deployment patterns, and training programs that raise AI fluency across the company. You’ll also help teams adopt AI coding assistants, agentic workflows, MCP servers, internal tools, and shared knowledge systems in ways that are useful, measurable, and safe around patient data. This is a hands-on builder role with a major enablement component. You should be as comfortable writing production-quality Python or TypeScript as you are running a workshop, facilitating office hours, or helping an operations lead understand how AI can improve a manual workflow. The ideal candidate is a builder, teacher, and systems thinker who measures success by what the whole organization can now do because of the tools, patterns, and examples you created. 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 Help define and drive Sprinter’s AI enablement strategy across engineering, operations, clinical, data, finance, and other functions Embed with teams to understand their workflows, identify high-leverage AI use cases, and translate business needs into working technical solutions Build bespoke agents, background workflows, internal tools, and automations that solve real operational, clinical, and engineering problems Create reusable playbooks, prompt libraries, skill libraries, workflow templates, and reference architectures that teams can self-serve Stand up shared context and knowledge systems that help AI tools ground answers in Sprinter’s data, documentation, codebases, and organizational context Evaluate, configure, and recommend AI tools, making practical build-versus-buy decisions based on team needs, safety, scalability, and cost Tune AI coding assistants and agentic workflows to Sprinter’s codebases, conventions, and development practices Build evaluation sets, benchmarks, and review patterns that help teams separate useful AI outputs from convincing-but-wrong ones Establish safe, repeatable deployment patterns for AI-built applications, internal tools, models, workflows, and data tables Partner with SRE, IT, Security, Legal, and clinical stakeholders on tool approval, deployment, access patterns, and PHI-safe guardrails Run recurring office hours, trainings, hackathons, and hands-on enablement sessions that build AI fluency across the company Measure AI adoption, productivity gains, quality improvements, and operational impact in ways that go beyond usage or token counts Communicate AI strategy, adoption progress, risks, and opportunities to individual contributors, managers, and executive leadership Help non-experts move quickly while ensuring patient safety, privacy, and quality are built into the workflow from the start What you have done Built production-quality software in Python, TypeScript, or similar languages Worked hands-on with LLMs, AI assistants, agents, tool calling, structured outputs, RAG, or other applied AI patterns Built internal tools, automations, workflows, developer productivity tooling, AI-enabled applications, or agentic systems Designed practical evaluations, benchmarks, or QA processes for AI workflows or software systems Worked with CI/CD, testing, deployment pipelines, or production release processes Gathered