Applied AI Engineer, Beneficial Deployments (Life Sciences)

Anthropic•San Francisco, CA

1d•$280,000 - $320,000•Hybrid

About The Position

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. The Beneficial Deployments team ensures AI reaches and benefits the communities that need it most by partnering with nonprofits, foundations, and mission-driven organizations to deploy Claude in areas like education, global health, economic mobility, and life sciences. This role focuses on maximizing the impact of Claude in the life sciences, aiming to accelerate scientific progress from R&D through translation. The engineer will work directly with flagship research partners, embedded in their scientific workflows, prototyping agents and developing ecosystem-level tooling. This is a founding role on the Beneficial Deployments applied AI team.

Requirements

4+ years as a Software Engineer, Forward Deployed Engineer, or technical founder — with production experience shipping systems that real users depend on.
Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.
Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.
Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder.
A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission.

Responsibilities

Partner deeply with flagship life sciences research institutions — understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day.
Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch.
Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research.
Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding.