Applied Scientist, AI

About Sprinter HealthAt 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 RoleWe’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 LocationWe 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 doTurn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasksBuild strong baselines and improve on them efficiently using the right modeling approach for the problemDevelop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriateDesign offline and online evaluations that are honest, measurable, and predictive of real-world impactChoose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomesRun careful error analysis and use it to improve model quality, product fit, and operational usefulnessIdentify label leakage, selection bias, confounding, and other data artifacts before they reach productionExplore messy real-world data, assess label quality, and determine whether a problem is ready for modelingPartner with ML engineering to productionize models reliably and define what production-readiness requiresWork with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge casesExplain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teamsWrite experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systemsPressure-test whether results are real, robust, and useful before recommending production useWhat you have doneBuilt, evaluated, and iterated on machine learning or AI models for real-world use casesTurned ambiguous business, product, clinical, or operational problems into measurable modeling tasksDesigned rigorous offline evaluations, experiments, or analyses that informed production or product decisionsWorked with messy real-world datasets where labels, outcomes, and causal relationships are imperfectUsed statistical reasoning, experimental design, and error analysis to understand model performanceBuilt models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, Matplotlib, or similarCompared modeling approaches and made pragmatic decisions about when to use traditional ML, LLMs, heuristics, or simpler baselinesCommunicated model performance, limitations, tradeoffs, and uncertainty to technical and non-technical stakeholdersPartnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impactOperated with enough engineering depth to run experiments end to end and self-serve deployments or production handoffs when neededUsed AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflowWhat gives you an edgeYou have an MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative fieldYou have exceptional applied experience that substitutes for formal graduate trainingYou have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AIYou’ve shipped models that reached production and had measurable real-world impactYou’ve worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement dataYou have experience working with PHI, HIPAA-aware systems, or other sensitive regulated dataYou know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right toolYou have experience collaborating with clinicians, clinical operations teams, or other high-stakes domain expertsYou’ve worked in a startup or fast-moving applied environment where ambiguity, speed, and rigor all matteredWhat makes you successfulYou understand how ML models work under the hood and can explain them clearly to non-technical stakeholdersYou focus relentlessly on impact and know that the simplest model is often the best oneYou treat evaluation as one of the most important parts of model developmentYou notice when a metric is misleading, incomplete, or disconnected from real-world outcomesYou catch leakage, bias, and confounding that others missYou move fluidly between modeling, error analysis, stakeholder partnership, and production handoffYou can hand a model to engineering and explain its limits to a clinician with equal clarityYou are comfortable with ambiguity and can adapt modeling approaches to problems that do not come with a playbookYou balance scientific rigor with the practical need to ship useful systemsDay to DayIn this role, you might spend your time:Exploring data and labels for a new healthcare or operational problemTurning an ambiguous product question into a measurable modeling taskBuilding and comparing models, then running error analysisReviewing misclassified or low-confidence cases with a clinical subject-matter expertDesigning an offline evaluation that is more likely to predict online or real-world successPartnering with ML engineering to prepare a model for deploymentWriting an experiment doc and presenting findings to product and leadershipPressure-testing whether a result is real or an artifact of the dataComparing a simple baseline, traditional ML model, and LLM-based approach to determine what is most usefulInvestigating why model performance differs across populations, workflows, labels, or operational contextsThe Interview ProcessWe aim to complete the interview process within 2–3 weeks. It will usually consist of:Recruiter Screen: Background fit, motivation, and compensation alignmentHiring Manager Interview: Applied science experience, modeling depth, and healthcare/product orientationHands-on Technical Assessment: Practical modeling, evaluation, error analysis, and scientific judgmentOnsite Interview: Technical case study, research or project presentation, behavioral interview, and lunch with the teamReferences: Validation of performance, judgment, and working styleWhat we offerMeaningful pre-IPO equityMedical, dental, and vision plans 100% paid for you and your dependentsFlexible PTO + 10 paid holidays per year401(k) with match16-week parental leave policy for birthing parent, 8 weeks for all other parentsHSA + FSA contributionsLife insurance, plus short and long-term disability coverageFree daily lunch in-officeAnnual learning stipendRelocation assistanceEqual Opportunity StatementSprinter 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 - $260KLocationSan Francisco, CAAddress394 Pacific Avenue , San Francisco, California, 94111Employment TypeFull timeLocation TypeHybridDepartmentSprinter HealthEngineeringCompensationSF Bay AreaEstimated Base Salary $180K – $260K • Offers Equity