Senior/Staff AI Enablement Engineer, Sprinter Health

• 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
• 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 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

Desirable

• 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
• 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 makes you successful:
• 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