Applied AI Engineer, Beneficial Deployments (Life Sciences)

Anthropic•San Francisco, NY

4d•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 by making Claude the go-to tool for the life sciences ecosystem. The engineer will work directly with research partners, embedding in their scientific workflows, prototyping agents, and developing ecosystem-level tooling. This position is part of the founding Beneficial Deployments Applied AI team focused on bringing life sciences closer to the frontier.

Requirements

Deep research experience in life sciences, biomedical research, or scientific computing.
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.

Nice To Haves

Studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.

Responsibilities

Partner deeply with flagship life sciences research institutions to 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, such as 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 challenges in 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 allows partners to self-serve, enabling what works for one institution to scale globally without the same level of hand-holding.