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Partner Manager, Global Health at Anthropic - ApplyBlast

Partner Manager, Global Health

Anthropic

3 months ago

3.7 on Glassdoor

San Francisco, CA

Hybrid

Full Time

Senior

0 applicants

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About this role

Anthropic is seeking a Partnerships Manager to drive the clinical AI research agenda for global health initiatives, ensuring Claude is safely deployed in low- and middle-income countries. This role involves defining validation frameworks for clinical AI tools, developing outcome metrics, and building research partnerships with academic and regulatory bodies. The position requires a hands-on approach to translating real-world care delivery realities into product improvements and evidence generation.

Responsibilities

Own the clinical research and evaluation agenda for global health work
Design clinical evaluations and validation frameworks for LLMs in LMIC contexts
Develop theories of change connecting model capability to care quality
Build and manage global research partnerships with academic and regulatory bodies
Partner with internal teams to improve Claude for clinical use cases in low-resource settings

Skills

Qualifications

MD, GP, MBBS, DO, or equivalent medical trainingExperience delivering care in low-resource settingsDirect experience evaluating or validating clinical AI/ML toolsDeep expertise in clinical research and evidence generationStrong command of regulatory landscape for clinical AI

About Anthropic

anthropic.com

Company Size

51-200 employees

View Profile

Anthropic is an AI safety and research company focused on building reliable, interpretable, and steerable AI systems. It develops large language models (branded as Claude) and offers APIs and enterprise products that let organizations integrate conversational AI with safety-focused controls, moderation, and privacy features. The company prioritizes interpretability and alignment research, publishes technical work, and engages with policymakers to reduce risks from advanced AI. Customers choose Anthropic for its safety-first approach, controllability tools, and research-driven models.