Applied AI Engineer, Life Sciences (Beneficial Deployments)

About Beneficial Deployments:

Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences — focusing on raising the floor for those who need it most.

About the Role:

We're looking for an Applied AI Engineer to join our Beneficial Deployments team, focused on maximizing the impact of Claude in the life sciences. Our goal is ambitious: accelerate scientific progress from R&D through translation by an order of magnitude. That means making Claude the go-to tool for the life sciences ecosystem from early discovery in academia to paradigm shifting biotech to reimaging pharma pipelines  — and building the technical infrastructure to back that up.

You'll work directly with flagship research partners like Howard Hughes Medical Institute and The Allen Institute, embedded in their scientific workflows. This isn't consulting from the outside — you'll be building alongside their engineers, prototyping agents that fit into real research pipelines, and developing the ecosystem-level tooling (MCP servers, benchmarks, reusable agent skills) that extends Claude's usefulness across the broader life sciences community. This role will be part of the founding Beneficial Deployments applied AI team focused on bringing more of life sciences closer to the frontier and be responsible for building with our partners. 

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.

You Might Be a Good Fit If You Have:

• 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.