Manager, Applied AI Engineering, Beneficial Deployments (Life Sciences)
San Francisco, CA | New York City, NY
Posted 16d ago
About the job
Anthropic is building reliable, interpretable, and steerable AI systems to be safe and beneficial for users and society. This role focuses on accelerating biological discovery and drug development through AI. As the Applied AI Engineering Manager for Life Sciences, you will lead a team of engineers who deploy AI solutions for scientific organizations. You will work closely with customers to understand their workflows and build prototypes, integrations, and agents that enable AI to perform meaningful work in labs and clinics. This is a hands-on leadership position requiring you to grow and manage the team, ensure technical success with strategic accounts, and translate field learnings into product improvements. The role involves building robust infrastructure to make messy biological data reliably accessible to AI agents, ensuring correctness, reproducibility, and auditability of results.
Responsibilities
- Build, hire, coach, and develop a team of Applied AI Engineers focused on life sciences partners.
- Set a high technical bar and foster engineer growth.
- Own the technical outcomes of strategic pharma and biotech deployments from scoping to production.
- Review and contribute to prototypes, integrations, agentic workflows, and code solutions.
- Guide the team in building deterministic tools, connectors, and evaluations for biological data and workflows.
- Partner cross-functionally to translate deployment learnings into product and model improvements.
- Set standards for responsible AI deployment in sensitive domains, enabling science while preventing misuse.
- Leverage knowledge of frontier models and R&D to solve life sciences problems.
Requirements
- Experience leading or technically mentoring software/ML engineers, preferably in a customer-facing role.
- Background in pharma, biotech, computational biology, bioinformatics, or clinical/regulatory affairs.
- Strong hands-on engineering background with experience in production code.
- Experience delivering technical work directly with external customers or partners.
- Ability to communicate credibly with technical experts and executives.
- Experience building on top of large language models or agents.
- Ability to quickly learn and master unfamiliar technical domains.
- High standards for reliability and reproducibility in scientific work.
- Experience building tooling, data infrastructure, evals, or agent harnesses for messy real-world data, especially in scientific settings.
- Commitment to safe and beneficial AI deployment in sensitive domains.