Manager, Applied AI Engineering, Life Sciences (Beneficial Deployments)

About the role:

Biology is the area where scientific progress has perhaps the greatest potential to directly and unambiguously improve human life — and we believe powerful AI could meaningfully accelerate the rate of biological discovery, helping compress decades of progress into years. With Claude for Life Sciences, we're building tools that accelerate the work of scientists and drug developers across the full lifecycle, from early discovery through clinical translation and regulatory review.

As the Applied AI Engineering Manager for Life Sciences, you'll lead the team of engineers who turn that ambition into deployed reality inside the world's leading scientific organizations. Our Applied AI Engineers are the technical front line: they sit with customers, understand their scientific and regulatory workflows in depth, and build the prototypes, integrations, and agents that let Claude do meaningful work in the lab and the clinic. You'll grow and lead this team, own the technical success of our most strategic life sciences accounts, those ties to advancing our strategy and mission, and create the feedback loop that turns what we learn in the field into better products and models.

This is more than wiring up a chatbot. As our own research has shown, the hard part of putting agents to work in biology is the infrastructure beneath them: messy databases, idiosyncratic file formats, scattered APIs, and metadata conventions where a single wrong or missing record can change a scientific conclusion. Your team builds the deterministic tools, connectors, and evaluations that make biological data reliably accessible to agents — and holds the work to a research-grade bar, where an answer has to be correct, reproducible, and auditable, not just plausible.

This is a hands-on leadership role. You'll coach engineers and raise the bar on their work, stay close enough to the technology to review prototypes and contribute directly, and partner across go-to-market, product, and research. Because this work sits in a sensitive, dual-use domain, you'll also help set the standard for how we deploy responsibly — enabling legitimate science while taking safety seriously.

In this role, you will:

• Build and lead the team: hire, coach, and develop a team of Applied AI Engineers dedicated to strategic life sciences partners, setting a high technical bar and helping each engineer grow.

• Own technical success with partners: be accountable for the technical outcomes of our strategic pharma and biotech deployments, from first scoping conversation through production.

• Stay hands-on: review and contribute to prototypes, MCP integrations, agentic workflows, and Claude Code for Bio solutions; help the team get unblocked on the hardest technical problems.

• Build agent-ready scientific infrastructure: guide the team in creating the deterministic tools, connectors/harnesses, and evaluations that make messy biological data and workflows reliably accessible to Claude — in partnership with scientists and research institutions.

• Translate the field into the roadmap: partner cross functionally to turn what you learn from deployments into improvements in Anthropic's life sciences products and models.

• Set the standard for responsible deployment: work alongside our safety teams to enable beneficial scientific work while guarding against misuse in a dual-use domain.

• Build for the frontier: use deep knowledge of frontier model intelligence coupled to your work in R&D and research to rapidly progress toward solutions to meaningful problems in life sciences. 

You may be a good fit if you:

• Have led or technically mentored software/ML engineers, ideally in a forward-deployed, solutions, or customer-facing engineering setting.

• Have a background in pharma, biotech, computational biology, bioinformatics, or clinical/regulatory affairs.

• Have a strong hands-on engineering background and are comfortable reading and writing production code, not just managing those who do.

• Have delivered technical work directly with external customers or partners, and can communicate credibly with both technical experts and executives.

• Have built on top of large language models or agents 

• Are energized by an unfamiliar technical domain and have a track record of going deep fast.

• Hold a high bar for reliability and reproducibility, and understand why a plausible-looking answer that's subtly wrong can be worse than no answer in scientific work.

• Have built tooling, data infrastructure, evals, or agent harnesses that turn messy real-world data into something usable and trustworthy — especially welcome if in a scientific or research setting.

• Care deeply about the safe and beneficial deployment of AI, especially in sensitive domains.

Strong candidates may also have:

• Experience deploying LLM or agent systems in regulated or enterprise environments.

• Experience building MCP servers, developer tooling, or scientific computing pipelines.

• Experience scaling a customer-facing technical team through a period of rapid growth.