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OpenEvidence is a medical AI platform used by >65% of US clinicians. The company is scaling globally to low- and middle-income countries (LMICs) where access to high-quality medical expertise is most limited. This role owns the clinical research and evaluation agenda for OpenEvidence's global health expansion.
You will design clinical evaluations and validation frameworks that establish how OpenEvidence performs in LMIC contexts, covering accuracy, safety, multilingual performance, and real-world conditions. Working alongside research, evaluation, and product teams, you will develop theories of change and outcome metrics connecting OpenEvidence's capabilities to care quality, health-worker performance, and patient outcomes. You will shape the safeguards that make the product safe and usable at the point of care, staying grounded in how care is actually delivered in low-resource settings and translating insights into product improvements, not just evaluations.
You will lead the clinical domain for a small, tight-knit global health team. While you own this domain, you should expect to roll up your sleeves on adjacent workstreams, help set overall strategy, and be a thought partner to colleagues working on other parts of the health system. Approximately 25% of your time will be spent at research and partner sites.
The company operates in-person in Miami and SF, with regular travel to research and partner sites. OpenEvidence provides meals, transport, and other support to optimize focus and productivity.
REQUIREMENTS:
- Medical training and clinical practice (MD, GP, MBBS, or equivalent) with direct experience delivering care
- Ability to reason fluidly from how diagnosis, triage, treatment, and referral actually happen at the bedside
- Concrete, on-the-ground understanding of clinical and care-delivery workflows in LMICs
- Direct experience evaluating or validating clinical AI/ML tools; understanding of the gap between benchmark performance and real-world clinical safety
- Deep expertise in clinical research and evidence generation for digital health or AI tools, with a clear view of what counts as credible evidence for safety and effectiveness
- Strong command of the regulatory and normative landscape for clinical AI, including WHO processes and national regulatory authorities in LMICs
- Track record of building research partnerships with academic and in-country researchers
- Preferred: experience at a healthtech or AI company in clinical validation, clinical quality, or medical affairs; publication record in digital health, clinical AI evaluation, or implementation science; direct clinical experience in a low- or middle-income country with humanitarian or global-health delivery organizations
