AI Enablement Engineer (Senior / Staff)

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 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.

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.

• 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

• 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 requirements from non-technical stakeholders and translated them into scoped, working technical solutions

• Enabled teams through documentation, training, office hours, workshops, hackathons, or reusable templates

• Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your day-to-day development workflow

• Made practical tradeoffs between speed, safety, usability, maintainability, and cost

• Communicated technical concepts clearly to audiences ranging from engineers to executives

• Operated in fast-moving, ambiguous environments where the path was not already defined

• You have operated at Senior, Staff, or equivalent scope, driving technical decisions across multiple teams

• You’ve built internal AI platforms, agent frameworks, evaluation systems, workflow automation platforms, or developer productivity tooling

• You’ve helped a company or team adopt AI tools in a measurable, repeatable way

• You have experience standing up a centralized prompt library, skill library, workflow library, or knowledge/context hub

• You’ve worked with MCP servers, internal tool integrations, RAG systems, or AI agents connected to real business systems

• You have experience with healthcare data, PHI, HIPAA-aware workflows, or regulated environments

• You’ve partnered with security, IT, legal, compliance, or clinical teams to approve and deploy AI tools safely

• You have a public or internal track record of teaching, writing, workshops, talks, or training that made complex technical ideas accessible

• You’ve worked in a startup or high-growth environment where enablement, velocity, and practical judgment mattered

• You are a force multiplier and measure success by what the whole organization can now build with AI

• You meet teams where they are, ship the first working example, and turn it into a template others can reuse

• You reach for the simplest tool that safely solves the workflow

• You build for safety from the start through guardrails, evaluations, review patterns, and PHI-aware defaults

• You back adoption claims with evidence, including evals, benchmarks, productivity metrics, and quality improvements

• You teach as well as you build

• You can make AI make sense to an engineer, an operations lead, a clinician, and an executive

• You help people move faster without making patient safety or privacy someone else’s problem

• You create systems that make good AI usage easier and risky AI usage harder

In this role, you might spend your time:

• Pairing with an operations, clinical, engineering, or finance team to turn a manual workflow into a reliable AI-assisted process

• Building a self-testing agent or background workflow that solves a recurring internal problem

• Running AI office hours, facilitating a hackathon, or leading a hands-on training session

• Creating a reusable template, skill, prompt library, or workflow pattern for a common task

• Building an eval set with a team to test whether an AI workflow gives correct-first-time answers

• Setting up CI checks or deployment pipelines so AI-built applications and internal tools ship safely

• Evaluating a new AI tool and making a build-versus-buy recommendation

• Instrumenting adoption and reporting real productivity gains to leadership

• Writing guardrails, documentation, or review patterns that help non-experts move quickly and safely

• Partnering with IT, Security, SRE, or Legal to approve and deploy AI tools responsibly

We aim to complete the interview process within 2–3 weeks. It will usually consist of:

• Recruiter Screen: Background fit, motivation, and compensation alignment

• Hiring Manager Interview: AI enablement experience, technical depth, and cross-functional scope

• Hands-on Technical Assessment: Practical AI workflow building, software engineering, evaluation, and implementation ability

• Onsite Interview: Systems design, technical case study, behavioral interview, and lunch with the team

• References: Validation of performance, judgment, and working style

• Meaningful pre-IPO equity

• Medical, dental, and vision plans 100% paid for you and your dependents

• Flexible PTO + 10 paid holidays per year

• 401(k) with match

• 16-week parental leave policy for birthing parent, 8 weeks for all other parents

• HSA + FSA contributions

• Life insurance, plus short and long-term disability coverage

• Free daily lunch in-office

• Annual learning stipend

• Relocation assistance

Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes.

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