{"id":16938,"url":"https://alion.io/job/sprinter-health-applied-scientist-ai","title":"Applied Scientist, AI","company":{"id":5046,"name":"Sprinter Health","domain":"sprinterhealth.com","url":"https://alion.io/company/sprinter-health","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":72,"open_postings":38,"ghost_share":0.553,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":20,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":260000,"currency":"USD","period":"year","gross":null,"usd_annual":260000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"HIPAA","optional":false},{"name":"Hugging Face","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Polars","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false}],"status":"live","first_seen_at":"2026-07-20T22:14:24Z","employer_posted_date":"2026-07-20","last_verified_at":"2026-11-05T11:15:56Z","board_verified":true,"closed_at":null,"days_open":107,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":107},"description":"About Sprinter Health\nAt 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.\nBy 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.\nAbout the Role\nWe’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.\nThis 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.\nThe 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.\nThis role is ideal for a scientist-engineer who can move fluidly between data exploration, modeling, experimentation, error analysis, stakeholder partnership, and production handoff.\nOffice Location\nWe 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.\nWe 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.\nLunch 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.\nWhat you will do\nTurn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasks\n\nBuild strong baselines and improve on them efficiently using the right modeling approach for the problem\n\nDevelop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriate\n\nDesign offline and online evaluations that are honest, measurable, and predictive of real-world impact\n\nChoose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes\n\nRun careful error analysis and use it to improve model quality, product fit, and operational usefulness\n\nIdentify label leakage, selection bias, confounding, and other data artifacts before they reach production\n\nExplore messy real-world data, assess label quality, and determine whether a problem is ready for modeling\n\nPartner with ML engineering to productionize models reliably and define what production-readiness requires\n\nWork with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases\n\nExplain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teams\n\nWrite experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systems\n\nPressure-test whether results are real, robust, and useful before recommending production use\n\nWhat you have done\nBuilt, evaluated, and iterated on machine learning or AI models for real-world use cases\n\nTurned ambiguous business, product, clinical, or operational problems into measurable modeling tasks\n\nDesigned rigorous offline evaluations, experiments, or analyses that informed production or product decisions\n\nWorked with messy real-world datasets where labels, outcomes, and causal relationships are imperfect\n\nUsed statistical reasoning, experimental design, and error analysis to understand model performance\n\nBuilt models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, Matplotlib, or similar\n\nCompared modeling approaches and made pragmatic decisions about when to use traditional ML, LLMs, heuristics, or simpler baselines\n\nCommunicated model performance, limitations, tradeoffs, and uncertainty to technical and non-technical stakeholders\n\nPartnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impact\n\nOperated with enough engineering depth to run experiments end to end and self-serve deployments or production handoffs when needed\n\nUsed AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflow\n\nWhat gives you an edge\nYou have an MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field\n\nYou have exceptional applied experience that substitutes for formal graduate training\n\nYou have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI\n\nYou’ve shipped models that reached production and had measurable real-world impact\n\nYou’ve worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data\n\nYou have experience working with PHI, HIPAA-aware systems, or other sensitive regulated data\n\nYou know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool\n\nYou have experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts\n\nYou’ve worked in a startup or fast-moving applied environment where ambiguity, speed, and rigor all mattered\n\nWhat makes you successful\nYou understand how ML models work under the hood and can explain them clearly to non-technical stakeholders\n\nYou focus relentlessly on impact and know that the simplest model is often the best one\n\nYou treat evaluation as one of the most important parts of model development\n\nYou notice when a metric is misleading, incomplete, or disconnected from real-world outcomes\n\nYou catch leakage, bias, and confounding that others miss\n\nYou move fluidly between modeling, error analysis, stakeholder partnership, and production handoff\n\nYou can hand a model to engineering and explain its limits to a clinician with equal clarity\n\nYou are comfortable with ambiguity and can adapt modeling approaches to problems that do not come with a playbook\n\nYou balance scientific rigor with the practical need to ship useful systems\n\nDay to Day\nIn this role, you might spend your time:\nExploring data and labels for a new healthcare or operational problem\n\nTurning an ambiguous product question into a measurable modeling task\n\nBuilding and comparing models, then running error analysis\n\nReviewing misclassified or low-confidence cases with a clinical subject-matter expert\n\nDesigning an offline evaluation that is more likely to predict online or real-world success\n\nPartnering with ML engineering to prepare a model for deployment\n\nWriting an experiment doc and presenting findings to product and leadership\n\nPressure-testing whether a result is real or an artifact of the data\n\nComparing a simple baseline, traditional ML model, and LLM-based approach to determine what is most useful\n\nInvestigating why model performance differs across populations, workflows, labels, or operational contexts\n\nThe Interview Process\nWe aim to complete the interview process within 2-3 weeks. It will usually consist of:\nRecruiter Screen: Background fit, motivation, and compensation alignment\n\nHiring Manager Interview: Applied science experience, modeling depth, and healthcare/product orientation\n\nHands-on Technical Assessment: Practical modeling, evaluation, error analysis, and scientific judgment\n\nOnsite Interview: Technical case study, research or project presentation, behavioral interview, and lunch with the team\n\nReferences: Validation of performance, judgment, and working style\n\nWhat we offer\nMeaningful pre-IPO equity\n\nMedical, dental, and vision plans 100% paid for you and your dependents\n\nFlexible PTO + 10 paid holidays per year\n\n401(k) with match\n\n16-week parental leave policy for birthing parent, 8 weeks for all other parents\n\nHSA + FSA contributions\n\nLife insurance, plus short and long-term disability coverage\n\nFree daily lunch in-office\n\nAnnual learning stipend\n\nRelocation assistance\n\nEqual Opportunity Statement\nSprinter 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.\nBeware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers.\nIf 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. 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