Applied AI Engineer, Life Sciences (beneficial Deployments)

Anthropic Anthropic · AI Frontier · New York, NY · Sales

Applied AI Engineer role focused on deploying Claude in life sciences to accelerate scientific progress. The role involves partnering with research institutions, building agents integrated into scientific workflows, and developing ecosystem infrastructure like MCP servers, benchmarks, and agent skills. The goal is to make Claude a go-to tool for the life sciences ecosystem, from discovery to pharma pipelines.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Skills

Required

  • Software Engineering
  • Forward Deployed Engineering
  • Technical Founder
  • Production experience shipping systems
  • Life sciences research
  • Biomedical research
  • Scientific computing
  • LLM-powered tools or applications
  • Prompting
  • Context engineering
  • Agent architectures
  • Evaluation frameworks

Nice to have

  • Genomics
  • Neuroscience
  • Drug discovery
  • MCP servers
  • Domain-specific data sources
  • Genomics platforms
  • Literature databases
  • Experimental repositories
  • Scientifically-grounded benchmarks
  • Reusable agent skills

What the JD emphasized

  • 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.

Other signals

  • deploying AI in life sciences
  • prototyping agents that fit into real research pipelines
  • developing ecosystem-level tooling
  • building with our partners