Anthropic
Applied AI Architect, Beneficial Deployments (Life Sciences Community & Enablement)
Owns scientific enablement for Claude across life sciences, running programs, demos, and a scientist community while scaling training to internal teams.
Captured October 2026Still open as of Oct 11, 2026 ↗
The role
This role sits within the Beneficial Deployments group's life sciences effort, which brings Claude to nonprofits and mission-driven partners. The person decides how scientists adopt Claude Science and related tools, through hands-on sessions and reusable materials. They also prepare internal customer-facing staff to carry that enablement forward without needing to be present.
What you would do
- Own the scientific enablement agenda end to end: choose initiatives, build materials, deliver them, and track whether research groups become daily users.
- Lead scientist activation efforts such as LSVP and Claude for Researchers, covering goals, program design, and execution.
- Create demos, notebooks, and guides that show how frontier models can advance a scientist's own work, and share them publicly on social platforms.
- Develop training and self-serve playbooks so Applied AI and Customer Success staff can support scientists independently.
- Establish and steward a community of scientists using Claude, serving as its public face and supporting ambassador efforts at events of varying size.
- Represent scientists' perspectives inside the company, turning their feedback into concrete requests and driving them to launch alongside marketing and communications.
What they asked for
- Practical familiarity with AI science tools such as Claude Science, including where they excel and where they fall short.
- Credible scientific background from academic labs, biotech, pharma, or research institutes, ideally using such tools yourself.
- Track record of driving tool adoption across many research teams through demos, training sessions, workshops, or hackathons.
- Current understanding of AI-for-science developments, including agentic analysis and AI co-scientist systems, and the ability to explain them to working scientists.
- Hands-on experience with LLM APIs sufficient to prototype a working demo; production-grade engineering is not required.
- Clear written and verbal communication for talks, tutorials, and documentation that scientists find genuinely useful.
- History of carrying initiatives from concept to measurable results, setting direction and owning outcomes, often without an existing playbook.
- Comfort working through ambiguity, structuring unclear problems, and taking on varied duties.
- Bachelor's degree or equivalent experience in a relevant field.
Nice to have
- Background designing or managing initiatives aimed at research audiences.
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