Applied AI Engineer, Beneficial Deployments; Life Sciences) Anthropic San Francisco, CA NY

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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.

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 The Howard Hughes Medical Institute (HHMI) 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 life sciences closer to the frontier.

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:

• 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.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $280,000—$320,000 USD

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in…