Clinician Scientist (MD)
Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Sep 9, 2026.
About UpDoc
AtUpDoc, 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.
Who You Are
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.
What You'll Do
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 capabilities.
Work with clinical data and EHR integrations, helping determine what information UpDoc needs, how EHR data should be interpreted, and how product workflows map onto real-world clinical systems.
Serve as a clinical and technical partner internally and externally, working with Product, Engineering, health-system informatics teams, and clinical stakeholders on integrations, implementation, and workflow questions.
What You'll Need
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.
Highly Desired
Experience developing or evaluating AI-enabled clinical products, including clinical evaluation datasets, rubrics, model evaluation, or AI safety and quality.
Experience in clinical research, clinical validation, or an FDA-regulated software environment.
Experience in chronic disease management, primary care, cardiometabolic health, or longitudinal care.
Clinical informatics experience or training; familiarity with EHRs, FHIR, healthcare interoperability, or health-system integrations.
Comfort with technical tools such as Python, SQL, notebooks, data analysis, or AI evaluation tooling.