Partner Manager, Global Health

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Beneficial Deployments Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, governments, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences. About The Role We're looking for a Partnerships Manager to drive forward the clinical AI research agenda for Anthropic's global health work — informing the tools that bring Claude safely into care and generating the evidence that proves they work. You’ll help define how clinical AI tools should be validated for use in low‑and‑middle‑income countries (LMICs), and you’ll help shape the tools and safeguards that make Claude safe and usable in clinical settings. This is a hands‑on role as much as a research one: you’ll work side by side with Anthropic’s research, evals, and product teams, translating what you know from how care actually gets delivered in low‑resource settings into evaluations, safeguards, and product improvements. You’ll join a small, tight‑knit global health team within Beneficial Deployments. While you’ll lead on this domain, you should expect to roll up your sleeves on adjacent workstreams, help shape overall team strategy, and be a thought partner to colleagues working on other parts of the health system. Everyone on the team owns the whole mission, not just their lane. Key Responsibilities Own the clinical research and evaluation agenda for our global health work — define what we need to prove, to what standard, and with whom, and drive it. Design clinical evaluations and validation frameworks for LLMs in LMIC contexts, covering accuracy, safety, multilingual performance, and real‑world conditions, in close partnership with Anthropic’s research, evals, and product teams. Develop theories of change and outcome metrics connecting model capability to care quality, health‑worker performance, and patient outcomes. Build and manage our global research partnerships, and engage with relevant regulatory and normative bodies (WHO, national authorities, research‑ethics bodies). Partner with internal research and product teams to improve Claude for clinical use cases in low‑resource settings. Stay grounded in how care is actually delivered in LMICs so our tools and evaluations reflect those realities. Contribute across the broader global health portfolio — lean in on adjacent workstreams, help set strategy, and be a thought partner to the team. Minimum Qualifications Medical training and clinical practice (MD, GP, MBBS, DO, or equivalent), with direct experience delivering care in low‑resource settings — you reason fluidly from how diagnosis, triage, treatment, and referral actually happen at the point of care in low and middle‑income countries. A concrete, on‑the‑ground understanding of clinical and care‑delivery workflows in LMICs, and what they mean for how AI tools must be designed and evaluated. Direct experience evaluating or validating clinical AI/ML tools — you understand 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 clinical safety and effectiveness. A strong command of the regulatory and normative landscape for clinical AI, including WHO processes, national regulatory authorities in LMICs, and research ethics. A track record of building research partnerships with academic and in‑country researchers, treating local partners as scientific collaborators rather than data‑collection sites. High agency, a strong team orientation, and comfort with ambiguity — you pitch in outside your lane and thrive building the plane while flying it. A genuine drive to maximize impact for underserved communities, and willingness to travel regularly to research and partner sites (~25%). Preferred Qualifications Hands‑on experience building or shaping AI/ML products or tooling — eval harnesses, agentic scaffolding, grounding and guardrails, or clinical decision‑support workflows. A publication record in digital health, clinical AI evaluation, or implementation science. Experience at a healthtech or AI company in clinical validation, clinical quality, or medical affairs. Experience working with philanthropic funders on evidence generation or research strategy. Direct clinical experience in a low‑ or middle‑income country, including with humanitarian or global‑health delivery organizations. Compensation Annual Salary: $215,000—$300,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience. Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience. Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position. Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren’t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. Equal Opportunity Statement We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. #J-18808-Ljbffr Anthropic