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 the top AI-native life sciences and biotech startups as they grow from early product to scale. Some of these companies build products on Claude that they sell to the rest of the industry, and others do the science themselves and want Claude woven into how their own research runs, and this role works with both. You'll lead deep technical engagements, partnering directly with startup engineering and scientific teams to help them build their products on Claude and run their own research with it, advising on implementation and agent design as they build what is newly possible with AI in the life sciences. You will bridge the gap between what Claude can do and what the life sciences need, working alongside some of the strongest teams in the field on problems that matter and pushing the boundaries of AI in science as you go.

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. You'll leverage your AI engineering expertise and your scientific domain knowledge to develop custom evaluation frameworks, design scalable architectures, and create the technical resources that enable life sciences startups to succeed with Claude.

Responsibilities

• Serve as a deep technical advisor to high-potential life sciences and biotech startups, whether Claude sits inside the product they sell or inside the way they make discoveries, 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 their scientists through pair programming and architecture reviews that accelerate their development, and understand their scientific workflows end to end so that your guidance fits into real research pipelines

• Develop prototypes and technical documentation, including evaluation suites, scientifically grounded benchmarks, AI engineering techniques, and architecture diagrams, that enable startups to build, operate, and scale with Claude

• Create reference implementations and sample code that many companies can adopt without starting from scratch, such as example MCP servers for domain-specific data sources like genomics platforms, literature databases, and experimental repositories, along with agent skills for common scientific workflows

• Collaborate closely with Applied AI Architects to maintain context and continuity across customer engagements

• Identify patterns across engagements, including what is hard about deploying AI in the life sciences such as heterogeneous data, auditability requirements, and the gap between a working prototype and a system that scientists trust, and contribute insights back to Product, Engineering, Research, and the broader Applied AI team, including feedback on the model capabilities and product features that would most accelerate discovery work

• Create technical content for startup audiences including documentation, tutorials, and sample code

• Represent Anthropic across the life sciences startup ecosystem and foster community engagement 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

You may be a good fit if you have

• Deep experience in the life sciences or biotech, gained through research or industry work in areas such as genomics, drug discovery, molecular design, lab automation, or clinical development, along with strong opinions of your own about how to bridge the gap between what AI can do today and what the life sciences need

• 4+ years of technical experience as a software engineer, ML or research engineer, computational biologist, research scientist, or technical founder, with 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 real scientific work in the lab or in research

• Experience with startups or high-growth companies and a love of working with founders and small teams that move quickly

• Strong technical communication skills, with the ability to translate complex AI concepts into architectural decisions and actionable implementation plans for both engineering and scientific audiences and to run technical workshops, hackathons, or developer-focused events

• A collaborative approach to working across teams, the ability to context-switch across scientific domains and use cases, and a passion for making powerful technology safe and beneficial