Clinical Lead, Beneficial Deployments

OpenEvidence is the most widely used medical AI platform in the world. In just over a year, our usage has grown to >65% of US clinicians via product-led word of mouth adoption. We are a $12B company with a 30 person engineering team from MIT, Harvard, and Stanford. We believe world-changing products come from a small group of exceptional, autonomous builders, organized along focused objectives and empowered to take individual ownership and run fast. We are now bringing OpenEvidence to clinicians and health workers in low- and middle-income countries, where access to high-quality medical expertise is scarcest and the stakes are highest. Doing that responsibly means proving, to a credible standard, that it is accurate, safe, and useful at the point of care in those settings. If you are a world-class clinician-scientist who wants to own that proof and shape how the best medical AI in the world is validated and adapted for the places that need it most, we want to talk. Culture We believe work is something that should be engaged with at a world-class level. Building 0->1 and scaling 1->1000 are professional sports, and uncompromising excellence is the bar. We believe building things that haven't existed is only possible with end to end ownership. We don't have separate project managers or study coordinators; you are responsible for defining what we need to prove, to what standard, and with whom, and for driving it to a result. Important things are accomplished when an individual decides to accomplish them. We believe that work should be hard. We believe good work is precise and intentional. Doing something a certain way just because that's how others in digital health are doing it guarantees mediocrity. We believe the quality of the product is paramount and the smallest details matter most, and nowhere more than in the gap between benchmark performance and real-world clinical safety. At the same time, velocity is essential. The best clinician-scientists know how to jump right to the hardest part of the problem and focus on patient safety. We believe your job is to complete and announce important evaluations and demonstrate their impact. Who are you? If you are looking to run studies someone else designed, this job is not for you. If you are looking to write papers as an end in itself, this job is not for you. If you are looking to lean in and get your hands dirty, bruised, and bloody building the clinical evidence base for medical AI in low-resource settings from nothing, shaping the safeguards at the point of care, and impacting hundreds of millions of lives, this job might be for you. The ideal candidate is a brilliant builder who is also a practicing clinician. They are smart, ambitious, scrappy, autonomous, precise, motivated, hard-working, and low-ego. They have medical training and clinical practice (MD, GP, MBBS, or equivalent) with direct experience delivering care, and they reason fluidly from how diagnosis, triage, treatment, and referral actually happen at the bedside. They have a concrete, on-the-ground understanding of clinical and care-delivery workflows in LMICs and what those workflows mean for how AI tools must be designed and evaluated. Does that sound rare? It is rare, and we would like to find them. The role You will own the clinical research and evaluation agenda for OpenEvidence in global health. You will design the clinical evaluations and validation frameworks that establish how OpenEvidence performs in LMIC contexts, covering accuracy, safety, multilingual performance, and real-world conditions, working side by side with our research, evaluation, and product teams. You will develop theories of change and outcome metrics that connect OpenEvidence's capabilities to care quality, health-worker performance, and patient outcomes, and you will help shape the safeguards that make the product safe and usable at the point of care. You will stay grounded in how care is actually delivered in low-resource settings and translate that into product improvements, not just evaluations. You will join a small, tight-knit global health team: you lead on this domain, but 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. You will spend roughly 25% of your time at research and partner sites. Skillsets Specific skillsets are secondary to the clinician-builder profile above, but some important spikes for different facets of this work are: Clinical AI evaluation You have direct experience evaluating or validating clinical AI/ML tools, and you understand the gap between benchmark performance and real-world clinical safety. You have 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. Evaluation and quantitative proof are first-class citizens to you. Regulatory and normative landscape You have a strong command of the regulatory and normative landscape for clinical AI, including WHO processes, national regulatory authorities in LMICs, and research ethics. Research partnerships You have a track record of building research partnerships with academic and in-country researchers, and you treat local partners as scientific collaborators rather than data-collection sites. You may have worked with philanthropic funders on evidence generation or research strategy. Industry and field experience You might have experience at a healthtech or AI company in clinical validation, clinical quality, or medical affairs, or a publication record in digital health, clinical AI evaluation, or implementation science. You might have direct clinical experience in a low- or middle-income country, including with humanitarian or global-health delivery organizations. We work in person in Miami and SF, and this role travels regularly (~25%) to research and partner sites. We believe in creating an environment that optimizes for focus and productivity, including providing meals, transport, and anything else you need to move quickly. #J-18808-Ljbffr OpenEvidence