Senior AI Enablement Engineer

Sprinter Health

AI Enablement Engineer at Sprinter Health driving AI adoption in internal workflows and automations. Collaborating across engineering, operations, clinical, and data teams to enhance operational efficiency.

Posted 7/20/2026full-timeSan Francisco • California • 🇺🇸 United StatesSenior💰 $180,000 - $260,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI enablement strategies, building internal tools, and automating workflows while ensuring safety and quality in clinical and operational contexts. Proficient in translating business needs into technical solutions and fostering AI fluency across diverse teams.

Highest-signal resume keywords

Python ProgrammingAI Assistant DevelopmentInternal Tool AutomationCI/CD Pipeline ManagementStakeholder Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

TypeScript ProgrammingLLM ApplicationBenchmark DesignQA Process DevelopmentAI Workflow Evaluation

Soft Skills

Training FacilitationDocumentation SkillsAdaptability in Ambiguous EnvironmentsClear Communication

Tools & Technologies

AI Coding AssistantsClaude CodeCursorDeployment PipelinesDeveloper Productivity Tools

Industry Keywords

AI EnablementOperational Impact MeasurementPatient SafetyPrivacy ComplianceWorkflow Automation

Tech Stack

Tools & technologies

PythonTypeScript

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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