UpDoc’s platform combines Clinical AI with physician coordination to deliver proactive, evidence-based care at scale. And the applications are limitless. Backed by Pear VC.
About the role
Define clinical reasoning, expected system behavior, and safety boundaries for UpDoc’s chronic disease products, including decision logic, uncertainty, risk, escalation, and safeguards. Build and run rigorous clinical evaluations—including scenarios, datasets, rubrics, and acceptance thresholds—and use failures and edge cases to drive improvements.
What they're looking for
- MD or DO degree, completion of residency training, board certification or eligibility, and meaningful direct patient-care experience
- Strong clinical judgment and the ability to reason rigorously about ambiguous cases, uncertainty, edge cases, and risk—and translate that reasoning into explicit, testable expectations
- Strong analytical skills and technical fluency, comfort working with data, APIs, EHR concepts, and AI systems
- Ability to work effectively with Engineering, AI/ML, Data, Product, clinical teams, and external health-system stakeholders
More about this role
At UpDoc , we are building the first clinically validated, physician-supervised AI agent that manages chronic diseases. We are an early-stage startup founded by Stanford physicians.
We are seeking a Clinician Scientist (MD) to define and evaluate the clinical intelligence behind UpDoc’s AI-enabled chronic disease products. You will work at the intersection of clinical reasoning, AI evaluation, and clinical data and EHR integrations.
This is a remote role.
You’re a physician with strong clinical judgment who thinks systematically about how clinical decisions are made and can make nuanced reasoning explicit enough to test in software. You’re comfortable working with data, APIs, EHR concepts, and AI systems, even without formal engineering experience.
Define clinical reasoning, expected system behavior, and safety boundaries for UpDoc’s chronic disease products, including decision logic, uncertainty, risk, escalation, and safeguards.
Build and run rigorous clinical evaluations—including scenarios, datasets, rubrics, and acceptance thresholds—and use failures and edge cases to drive improvements.
Contribute to clinical validation, outcomes analysis, and research supporting new...
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