AI Research Scientist

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.