Applied AI Engineer, Life Sciences (Beneficial Deployments)
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole.
What this role actually needs.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. 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. Requirements: - 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. Company context: Anthropic is an AI safety company building Claude, a frontier-model assistant for developers, enterprises, and consumers.
Day-to-day expectations
Anthropic lists these responsibilities for the Applied AI Engineer, Life Sciences (Beneficial Deployments) role.
- 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.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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.
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Source: job-boards.greenhouse.io · Source ID: 5111942008 · Confidence: 97/100 · Last checked: May 7, 2026
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