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 Applied Scientist, AI to turn messy, high-stakes healthcare problems into machine learning models and AI systems that improve access to care and help Sprinter operate more effectively.

This role sits at the intersection of research, product, engineering, and clinical operations. You’ll take ambiguous product and operational problems and turn them into well-scoped prediction, ranking, optimization, NLP, or LLM-based tasks. You’ll build strong baselines, design honest evaluations, run careful error analysis, and iterate toward models that can improve real-world outcomes.

The right person for this role combines scientific rigor with a deployment-oriented mindset. You should care deeply about evaluation, leakage, bias, confounding, and whether offline results actually translate into production impact. You should also be able to partner closely with ML engineering to productionize models, work with clinicians and subject-matter experts to validate assumptions, and explain model behavior, uncertainty, and limitations clearly to product and leadership.

This role is ideal for a scientist-engineer who can move fluidly between data exploration, modeling, experimentation, error analysis, stakeholder partnership, and production handoff.

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

Turn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasks
Build strong baselines and improve on them efficiently using the right modeling approach for the problem
Develop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriate
Design offline and online evaluations that are honest, measurable, and predictive of real-world impact
Choose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes
Run careful error analysis and use it to improve model quality, product fit, and operational usefulness
Identify label leakage, selection bias, confounding, and other data artifacts before they reach production
Explore messy real-world data, assess label quality, and determine whether a problem is ready for modeling
Partner with ML engineering to productionize models reliably and define what production-readiness requires
Work with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases
Explain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teams
Write experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systems
Pressure-test whether results are real, robust, and useful before recommending production use

What you have done

Built, evaluated, and iterated on machine learning or AI models for real-world use cases
Turned ambiguous business, product, clinical, or operational problems into measurable modeling tasks
Designed rigorous offline evaluations, experiments, or analyses that informed production or product decisions
Worked with messy real-world datasets where labels, outcomes, and causal relationships are imperfect
Used statistical reasoning, experimental design, and error analysis to understand model performance
Built models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, Matplotlib, or similar
Compared modeling approaches and made pragmatic decisions about when to use traditional ML, LLMs, heuristics, or simpler baselines
Communicated model performance, limitations, tradeoffs, and uncertainty to technical and non-technical stakeholders
Partnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impact
Operated with enough engineering depth to run experiments end to end and self-serve deployments or production handoffs when needed
Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflow

What gives you an edge

You have an MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field
You have exceptional applied experience that substitutes for formal graduate training
You have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI
You’ve shipped models that reached production and had measurable real-world impact
You’ve worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data
You have experience working with PHI, HIPAA-aware systems, or other sensitive regulated data
You know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool
You have experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts
You’ve worked in a startup or fast-moving applied environment where ambiguity, speed, and rigor all mattered

What makes you successful

You understand how ML models work under the hood and can explain them clearly to non-technical stakeholders
You focus relentlessly on impact and know that the simplest model is often the best one
You treat evaluation as one of the most important parts of model development
You notice when a metric is misleading, incomplete, or disconnected from real-world outcomes
You catch leakage, bias, and confounding that others miss
You move fluidly between modeling, error analysis, stakeholder partnership, and production handoff
You can hand a model to engineering and explain its limits to a clinician with equal clarity
You are comfortable with ambiguity and can adapt modeling approaches to problems that do not come with a playbook
You balance scientific rigor with the practical need to ship useful systems

Day to Day

In this role, you might spend your time:

Exploring data and labels for a new healthcare or operational problem
Turning an ambiguous product question into a measurable modeling task
Building and comparing models, then running error analysis
Reviewing misclassified or low-confidence cases with a clinical subject-matter expert
Designing an offline evaluation that is more likely to predict online or real-world success
Partnering with ML engineering to prepare a model for deployment
Writing an experiment doc and presenting findings to product and leadership
Pressure-testing whether a result is real or an artifact of the data
Comparing a simple baseline, traditional ML model, and LLM-based approach to determine what is most useful
Investigating why model performance differs across populations, workflows, labels, or operational contexts

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: Applied science experience, modeling depth, and healthcare/product orientation
Hands-on Technical Assessment: Practical modeling, evaluation, error analysis, and scientific judgment
Onsite Interview: Technical case study, research or project presentation, 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.

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