AI Enablement Engineer (Senior / Staff)

Sprinter Health • San Francisco, CA

2d • Hybrid

About The Position

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.

Requirements

• 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

Nice To Haves

• Operated at Senior, Staff, or equivalent scope, driving technical decisions across multiple teams
• Built internal AI platforms, agent frameworks, evaluation systems, workflow automation platforms, or developer productivity tooling
• Helped a company or team adopt AI tools in a measurable, repeatable way
• Have experience standing up a centralized prompt library, skill library, workflow library, or knowledge/context hub
• Worked with MCP servers, internal tool integrations, RAG systems, or AI agents connected to real business systems
• Have experience with healthcare data, PHI, HIPAA-aware workflows, or regulated environments
• Partnered with security, IT, legal, compliance, or clinical teams to approve and deploy AI tools safely
• Have a public or internal track record of teaching, writing, workshops, talks, or training that made complex technical ideas accessible
• Worked in a startup or high-growth environment where enablement, velocity, and practical judgment mattered

Responsibilities

• 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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed