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Salary: USD 275,000 - 315,000 / annual
Anthropic's Beneficial Deployments team is seeking an Applied AI Architect to lead scientific enablement and community building around Claude, particularly Claude Science. This role is the DRI (directly responsible individual) for all aspects of scientific enablement—from strategy and program design through execution and measurement of impact.
Key responsibilities include:
• Owning scientific enablement strategy: decide what programs to run, build materials, deliver workshops and hackathons, and measure whether research teams achieve daily use of Claude
• Leading large-scale activation programs such as the Life Sciences Verification Program (LSVP) and Claude for Researchers—setting goals, designing programs, and executing them
• Creating demos, notebooks, and tutorials showcasing frontier models for scientific workflows; actively engaging with the scientific community on social platforms (X, LinkedIn, etc.)
• Scaling enablement internally by training Anthropic's Applied AI and Customer Success teams and building playbooks they can execute independently
• Building and leading a scientist community around Claude, serving as ambassador and supporting ambassador programs from local meetups to major conferences
• Acting as the voice of scientists within Anthropic—translating community feedback into concrete product and marketing asks, partnering with Marketing and Communications to ensure campaigns reach the scientific community
You'll work at the intersection of product, community, and technical enablement, with significant autonomy to shape how Claude reaches and benefits the scientific research community.
REQUIREMENTS:
• Deep hands-on expertise with AI tools for science (especially Claude Science)—understanding where they excel and their limitations
• Real credibility as a scientist with hands-on experience doing science in academic labs, biotechs, pharma, or research institutes; ideally prior experience with similar tools
• Proven track record helping multiple scientific teams adopt new tools through demos, trainings, workshops, or hackathons
• Current knowledge of AI for science (agentic analysis, AI co-scientists) and ability to explain these concepts to working scientists
• Hands-on experience building with LLM APIs and prototyping working demos (production-level coding not required)
• Strong written and spoken communication skills—ability to create talks, tutorials, and documentation that scientists find valuable
• Track record of taking programs or products from concept to results, setting direction, executing, and being accountable for outcomes often without a playbook
• Comfort operating in ambiguity, bringing clarity to fuzzy problems, wearing multiple hats, and doing what it takes to further the mission
• Bachelor's degree or equivalent combination of education, training, and professional experience in a field relevant to the role
• Years of experience will correlate with internal job level requirements
