# AI Research Scientist

**Company:** [Sprinter Health](https://hotfix.jobs/companies/sprinter-health)
**Location:** San Francisco, CA, Menlo Park, CA
**Role:** AI Research
**Salary:** $160k – $220k/yr
**Skills:** LLMs, multimodal learning, causality, uncertainty estimation, clinical ai, Machine Learning, Python, PyTorch, TensorFlow, irb processes, Data Governance
**Posted:** 2026-07-17

> AI Research Scientist advancing methodological frontiers in healthcare AI at Sprinter Health. Own a research agenda, develop novel architectures/methods, publish at top venues, collaborate with clinicians, and translate findings into production systems. Requires deep ML expertise, strong research taste, and healthcare validation knowledge.

## Job Description

## 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.

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