Applied AI Engineer, Startups (Life Sciences)

Anthropic•San Francisco, CA

17h•$280,000 - $320,000•Hybrid

About The Position

As an Applied AI Engineer on Anthropic’s Startups team, you will serve as a trusted technical advisor to AI-native life sciences and biotech startups as they grow from early product to scale. You will work with companies building products on Claude and companies incorporating Claude into their own scientific research. You will lead technical engagements with startup engineering and scientific teams, advising on implementation and agent design and helping them build products and research workflows with Claude. Working with Applied AI Architects, Account Executives, and Anthropic’s Product, Engineering, and Research teams, you will help startups build at the frontier of what is possible with Claude. You will apply AI engineering expertise and scientific domain knowledge to develop evaluation frameworks, design scalable architectures, and create technical resources that support life sciences startups. The role includes customer-site travel for workshops, implementation guidance, and relationship building. Anthropic currently expects staff to work from one of its offices at least 25% of the time, although some roles may require more office attendance. The minimum education requirement is a bachelor’s degree or equivalent combination of education, training, and/or experience, in a field relevant to the role as demonstrated through coursework, training, or professional experience. Visa sponsorship may be available, subject to role and candidate circumstances.

Requirements

Deep experience in life sciences or biotech through research or industry work in areas such as genomics, drug discovery, molecular design, lab automation, or clinical development.
At least 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—including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale—or a track record of successfully applying AI to scientific work in a lab or research setting.
Strong technical communication skills, including the ability to translate complex AI concepts into architectural decisions and actionable implementation plans for engineering and scientific audiences.
Ability to run technical workshops, hackathons, or developer-focused events.
Collaborative approach to working across teams, ability to context-switch across scientific domains and use cases, and passion for making powerful technology safe and beneficial.
Bachelor’s degree or equivalent combination of education, training, and/or experience.
Relevant field of study demonstrated through coursework, training, or professional experience.

Nice To Haves

Experience with startups or high-growth companies.
Enjoyment of working with founders and small teams that move quickly.
Strong perspectives on bridging the gap between current AI capabilities and the needs of the life sciences.

Responsibilities

Serve as a deep technical advisor to high-potential life sciences and biotech startups, helping them build innovative Claude use cases for products and scientific research and development.
Work hands-on with startup engineering teams and scientists through pair programming and architecture reviews, and understand scientific workflows end to end.
Develop prototypes and technical documentation, including evaluation suites, scientifically grounded benchmarks, AI engineering techniques, and architecture diagrams.
Create reusable reference implementations and sample code, including example MCP servers for scientific data sources and agent skills for common scientific workflows.
Collaborate with Applied AI Architects to maintain context and continuity across customer engagements.
Identify patterns and challenges across engagements and share insights with Product, Engineering, Research, and the broader Applied AI team, including feedback on model capabilities and product features.
Create technical content for startup audiences, including documentation, tutorials, and sample code.
Represent Anthropic in the life sciences startup ecosystem through hackathons, webinars, technical office hours, in-person founder events, and research show-and-tells.
Travel to customer sites for workshops, implementation guidance, and relationship building.