Interested in this AI/ML Engineer role at Sprinter Health?
Apply Now →Skills & Technologies
About This 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 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
What gives you an edge
--------------------------
- 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
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
Day to Day
--------------
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
The Interview Process
-------------------------
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
What we offer
-----------------
- 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
Equal Opportunity Statement
-------------------------------
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.
Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers.
If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles on our official Sprinter Health Careers website. All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job\-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com.
Compensation Range: $180K \- $260K
Salary Context
This $180K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sprinter Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $180K to $260K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sprinter Health AI Hiring
Sprinter Health has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Menlo Park, CA, US, San Francisco, CA, US. Compensation range: $220K - $270K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.