About the 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 have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway.
About
the Role We’re looking for an AI Research Scientist to advance the methodological frontier of AI in healthcare. This role is ideal for someone who has demonstrated strong research taste, deep technical foundations, and the ability to turn open-ended problems into rigorous scientific contributions. You will develop and own a research agenda aligned with Sprinter’s company strategy. Your work may include novel architectures, new training or evaluation techniques, long-horizon research bets, peer-reviewed validation studies, patents, and methods that ultimately graduate into production systems. The ideal candidate is deeply technical, scientifically rigorous, and excited to collaborate closely with clinicians, product leaders, and applied AI teams. You understand that healthcare validation standards are higher than benchmark culture alone, and you are energized by the opportunity to produce research that is both scientifically meaningful and practically impactful. Hybrid & Office Experience 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: Research Agenda & Scientific Contribution Develop and own a research agenda aligned with Sprinter’s long-term AI and company strategy. Identify open problems, position them against the literature, and design experiments that isolate meaningful contributions. Develop novel methods, architectures, training approaches, evaluation techniques, or validation frameworks. Produce publications, patents, peer-reviewed validation studies, and other evidence artifacts. Translate promising research into methods and tools that applied teams can use in production. Technical Leadership Raise the scientific bar across applied AI and engineering teams. Review methodologies, evaluation approaches, and experimental designs. Advise teams on hard technical decisions, especially around model performance, reliability, evaluation, uncertainty, and validation. Help determine whether a result is meaningful, reproducible, or an artifact. Mentor applied researchers and engineers on rigorous ML research practices. External Presence & Collaboration Maintain an external research presence through publications, talks, academic collaborations, and participation in relevant research communities. Collaborate with clinical partners on validation studies, including work that may involve IRB review, data governance, external validation, or prospective evaluation. Partner cross-functionally with Product, Clinical, Engineering, and Leadership teams to ensure research priorities map to meaningful company and patient impact. What you have done: Demonstrated ability to produce novel research, including identifying open problems, designing rigorous experiments, and writing work to a peer-review standard. Deep ML foundations and genuine depth in at least one relevant area, such as LLMs, agents, uncertainty, causality, multimodal learning, clinical AI, or related fields. Strong engineering ability, including the ability to run your own experiments at scale. Strong research taste and the ability to distinguish incremental work from meaningful methodological contribution. Comfort working in open-ended, ambiguous environments where the right research direction may need to be shaped from first principles. Interest in clinical collaboration and applied healthcare impact. Understanding of healthcare validation standards, including the importance of external validation, prospective evaluation, data governance, and real-world deployment constraints. What gives you an edge: First-author publications at top technical venues such as NeurIPS, ICML, ICLR, ACL, or related conferences. Publications in leading clinical AI or healthcare venues such as Nature Medicine, NEJM AI, npj Digital Medicine, CHIL, MLHC, or similar. Experience in academia, industry research labs, or research-heavy teams at AI-native healthcare companies. Experience collaborating with clinicians, clinical researchers, or healthcare operators. Familiarity with IRB processes, clinical data governance, or healthcare model validation. Dual literacy across machine learning and clinical collaboration. Interview Process: We aim to complete the interview process between 2–3 weeks. It will usually consist of: Recruiter Screen (30 minutes) Hiring Manager Introduction (30 minutes) Hands-on-Keys Technical Assessment (1 hour) Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with