Clinical AI Research Fellowship

Kinetic Systems; United States; Remote; USD 61,000 - 76,000; Part time

Benefits offered by this job

API credits

GPU support

Mentorship & evaluation infra access

Access to evaluation infrastructure

Job summary

Kinetic Systems is a healthcare AI research lab offering a Clinical AI Research Fellowship for clinicians. The role is part-time, in-person in San Francisco or remote within the U.S., for 3–6 months with a minimum of 10 hours per week and a stipend of 1500 USD per week.

You will work with AI researchers, engineers, and clinicians to improve healthcare AI models and publish research. You will have opportunities to collaborate with frontier AI labs and academic institutions, contributing to

Qualifications

• Must be a clinician meeting the stated criteria.
• Experience handling sensitive healthcare data is preferred.
• Experience with reinforcement learning environments and evaluation rubrics is a plus, not required.

Responsibilities

• Develop novel tasks and benchmarks reflecting real-world healthcare workflows.
• Evaluate frontier models on complex clinical tasks and analyze failure modes.
• Design end-to-end experiments and propose new research directions.
• Partner with AI researchers to refine work.
• Participate in weekly check-ins and group presentations.

Skills

Experience with sensitive healthcare数据

RL environments knowledge

Education

Medical student (4th year) / Resident / MD / DO / NP / PA / RN / PharmD

Job description

Kinetic Systems is a healthcare AI research lab. We partner with frontier labs and healthcare organizations to help make AI models safer and more reliable.

We spun out of the Stanford PhD program in 2025 and are backed by General Catalyst, Conviction, and other great investors.

Our Clinical AI Research Fellowship is a selective program open to strong clinicians with an interest in shaping the future of AI in healthcare.

This opportunity is currently only for clinicians. You must meet the clinical criteria below to be considered.

• This is a part-time, in-person (San Francisco) or remote (U.S only) role.

• Duration: 3-6 months

• Time commitment: Minimum 10 hours per week

• Stipend: $1500 per week

• Resources: API credits, GPU support, human data budgets, mentorship & access to evaluation infrastructure

The Role

As a Clinical AI Research Fellow, you’ll work closely with AI researchers, engineers, and clinicians to help improve healthcare AI for patients and providers.

If selected as a Fellow, you will have the opportunity to work with frontier AI labs, leading academic institutions, and pioneers in the medical field to help shape the future of AI in healthcare. You will be given first priority as new research and project opportunities arise.

Your insights will directly impact AI models used by hundreds of millions of patients and providers every day. The standards you set will define how providers across the globe leverage these technologies to drive better care outcomes for their patients.

In addition to working with frontier AI labs, you will have the opportunity to publish peer-reviewed academic research with our team and earn the ability to participate in future projects.

Benefits

• Gain an unparalleled understanding of how AI will impact medicine and your career

• Learn directly from AI PhD researchers

• Co-author papers for peer-reviewed journals and ML conferences

• Curated invitations to researcher events & gatherings

What You’ll Do

• Develop novel tasks and benchmarks that reflect real-world healthcare workflows

• Evaluate frontier models on complex clinical tasks, and critically analyze their failure modes

• Design end-to-end experiments and propose novel research directions for assessing model capabilities

• Partner with leading AI researchers to refine and iterate on your work

• Participate in weekly check-ins and weekly group presentations

Who You Are

• Medical student (4th year), Resident (PGY-2+), MD, DO, NP, PA, RN or PharmD

• Experience working with sensitive healthcare data

• Prior background working with RL environments, evals, rubrics is a plus, but not required

Why Us

Our company is founded on a decade of healthcare AI research conducted at Stanford University at the intersection of AI, benchmarking, and healthcare. Our team has published work in leading peer-reviewed venues including NEJM AI, Nature Medicine, JAMIA, NeurIPS, ICLR, and VLDB. See some of our team's prior work here: https://kineticsystems.ai/blog