Kinetic Systems is an applied research lab building data infrastructure for the autonomous hospital. We spun out of the Stanford PhD program in 2025 and are backed by General Catalyst. Our mission is to advance the capabilities of frontier models for solving clinically and economically meaningful healthcare tasks.
The Role
As a Member of Clinical Staff, you’ll help drive our research agenda towards advancing AI model capabilities on real-world healthcare tasks. You will build reinforcement learning environments, novel datasets and benchmarks, publish academic papers, and post-train models to push the SOTA while staying grounded in the demonstrated needs of our healthcare partners.
This is not an annotation role. You'll design the work others do. The clinical judgment you encode into our data decides whether a model gets a real clinical workflow right, or gets it confidently wrong.
Note: This is a full-time role, required to be in-person in SF.
What You’ll Do
Design and build RL environments that capture real clinical work
Find where frontier models fall short on real clinical workflows, and turn those gaps into environments, evals, and benchmarks
Stress-test your own environments, hunting for shortcuts and reward hacks before a model finds them
Write the rubrics and guidelines that capture expert clinical judgment, especially where the right answer is subjective or high-stakes
Build gold-standard datasets, pilot new tasks yourself, and set the quality bar our clinician network works to
Lead calibration with clinician raters and adjudicate the hardest cases
Work with our ML team to turn training and evaluation needs into high-quality clinical data
Write and publish papers in academic conferences
Who You Are
Must have one of the following medical degrees: MD, DO, MBBS, NP, or PA
2+ years of experience working in a real clinical environment
You've done serious work in the human data space, at a frontier AI lab or a human data company (e.g., Scale AI, Surge AI, Mercor, Handshake AI, Turing). Not just completing tasks: you've designed evals, written the rubrics others worked from, led teams of clinical experts, or owned data that a frontier lab trained or evaluated on
You've built RL environments, evals, or graders yourself, not just read about them
You understand how data quality shapes model training, including preference data, rubric design, and rater agreement
You understand healthcare data standards (e.g., EHR data structures, interoperability, HIPAA considerations)
You're proficient in Python and JavaScript, with intermediate software engineering skills (i.e., not just Claude Code) and experience with GitHub, CI/CD, and AWS
You have experience with clinical workflow analysis, quality improvement initiatives, or digital health product development
Why Us
If you join our team, you will be joining a team with...
Founding-level impact and ownership
An opportunity to advance AI research in one of its most meaningful application areas
Competitive compensation and meaningful equity
Unlimited PTO
Comprehensive health, dental, and vision coverage
401(k)
Free lunch + dinner