Applied AI Engineer, Startups (Life Sciences)

About the Role

As an Applied AI Engineer on the Startups team at Anthropic, you will be a trusted technical advisor to top AI-native life sciences and biotech startups as they grow from early product to scale. You will lead deep technical engagements, partnering directly with startup engineering and scientific teams to help them build products on Claude and run their research with it, advising on implementation and agent design.

Working closely with Applied AI Architects, Account Executives, and Anthropic's Product, Engineering, and Research teams, you'll help startups build at the edge of what is possible with Claude.

Responsibilities

• Serve as a technical advisor to high-potential life sciences and biotech startups, working alongside them to build innovative use cases that push the boundaries of AI in scientific research and development.
• Work hands-on with startup engineering teams and scientists through pair programming and architecture reviews that accelerate development.
• Develop prototypes and technical documentation, including evaluation suites, scientifically grounded benchmarks, AI engineering techniques, and architecture diagrams.
• Create reference implementations and sample code, such as example MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories) and agent skills for common scientific workflows.
• Collaborate closely with Applied AI Architects to maintain context and continuity across engagements.
• Identify patterns across engagements regarding deploying AI in life sciences and contribute insights back to Product, Engineering, and Research teams.
• Create technical content for startup audiences including documentation, tutorials, and sample code.
• Represent Anthropic across the life sciences startup ecosystem through hackathons, webinars, technical office hours, and research show and tells.
• Travel to customer sites for workshops, implementation guidance, and relationship building.

Qualifications

• Deep experience in life sciences or biotech, gained through research or industry work in areas such as genomics, drug discovery, molecular design, lab automation, or clinical development.
• 4+ years of technical experience as a software engineer, ML or research engineer, computational biologist, research scientist, or technical founder.
• Either production experience building LLM-powered applications (prompting, context engineering, agent architectures, evaluation frameworks, deployment at scale) or a track record of successfully applying AI to scientific work.
• Experience with startups or high-growth companies.
• Strong technical communication skills, with the ability to translate complex AI concepts into architectural decisions for engineering and scientific audiences.
• Collaborative approach and the ability to context-switch across scientific domains and use cases.
• Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.