Applied AI Engineer for Life Sciences Deployments

Diiirect Inc.

New York (NY)

Hybrid

USD 280,000 - 320,000

Full time

5 days ago

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Job summary

Anthropic is seeking an Applied AI Engineer for the Beneficial Deployments team to maximize Claude's impact in life sciences. You will prototype agents, build ecosystem tooling, and collaborate with premier research partners to bring AI from discovery to translation.

You will work with institutions like HHMI and The Allen Institute, iterating in real research pipelines and shipping production code. A strong background in life sciences and LLM-powered applications is essential.

Qualifications

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.
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.