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

Anthropic•San Francisco, NY

16h•Hybrid

About The Position

As the Manager of Applied AI Engineering, Life Sciences, you will lead and grow a team of Applied AI Engineers focused on maximizing Claude's impact in the life sciences. You'll be responsible for establishing processes and best practices for the team's engagements with research institutions and life sciences organizations, helping each team member achieve success, high productivity, and career growth, and representing Anthropic as a technical leader on some of its most important scientific partnerships. You'll leverage your technical depth in both AI/ML and life sciences, combined with your leadership experience, to drive the strategy for how Claude transforms scientific workflows — from hypothesis generation and literature synthesis through experimental design, data analysis, and regulatory submission. In collaboration with the Sales, Product, Engineering, and Research teams, you'll help mission-driven organizations incorporate leading-edge AI into their scientific processes while maintaining our best-in-class safety standards. This is a founding leadership role: you'll shape the team's culture, define its operating model, and establish its reputation as a trusted partner in the scientific community.

Requirements

7+ years of experience engineering solutions in the life sciences or pharmaceutical industry within computational biology, biological data science, or similar engineering applications in life sciences
3+ years of engineering management experience, preferably leading teams that serve research-oriented customers
Deep domain expertise in one or more areas of the life sciences: drug discovery, genomics, computational biology, clinical research, translational science, or scientific data infrastructure
Experience working with research institutions, academic partners, nonprofits, or mission-driven organizations — understanding their unique workflows, constraints, and decision-making processes
Strong programming skills with proficiency in Python and experience building production AI/ML applications
An organizational mindset and enjoy building foundational teams in a relatively unstructured environment
Excellent communication, collaboration, and coaching abilities — able to translate between scientific and engineering contexts
Comfort dealing with highly uncertain, ambiguous, and fast-moving environments
Strong executive presence and ability to foster deep relationships with scientific leaders and research teams
At least a high-level familiarity with the architecture and operation of large language models and/or ML in general
A passion for making powerful technology safe and societally beneficial
Think creatively about the risks and benefits of AI in scientific research, and think beyond past checklists and playbooks
Stay up-to-date and informed by taking an active interest in emerging AI research, life sciences breakthroughs, and industry trends
We require at least a Bachelor's degree in a related field or equivalent experience.

Nice To Haves

PhD or advanced degree in a life sciences field (biology, biochemistry, computational biology, bioinformatics, or related)
Experience with AI-powered drug discovery, protein structure prediction, or genomic analysis platforms
Track record of building and scaling technical teams through rapid growth in a startup or high-growth environment
Experience with regulatory considerations in life sciences (FDA submissions, clinical trial data, GxP compliance)
Previous experience building developer tooling, SDKs, or platform capabilities for scientific communities
Established relationships and reputation within the life sciences research community

Responsibilities

Manage and mentor a team of Applied AI Engineers focused on life sciences, providing both technical guidance and career development
Set goals and reviews for your team, promoting growth, scientific rigor, and high-quality output
Serve as a deep technical partner to flagship life sciences research institutions — understanding their scientific workflows end to end and advising on where AI can meaningfully accelerate discovery
Drive the team's engagement strategy with partners like HHMI, Allen Institute, and other research organizations, ensuring high-impact, embedded collaboration
Build and prototype AI agents and tools that fit into real scientific research pipelines, including MCP servers, benchmarks, and reusable agent skills
Partner closely with Beneficial Deployments leadership to define the life sciences roadmap and co-build strategies that extend Claude's reach in drug discovery, genomics, clinical research, and translational science
Drive collaboration across cross-functional teams — Product, Engineering, Research, and Safety — to influence and unify stakeholders at all levels of the organization
Develop scalable engagement frameworks and reusable technical assets that can be adapted across different life sciences domains and partner contexts
Travel occasionally to partner institutions for workshops, technical deep-dives, and relationship building
Establish a shared vision for creating AI solutions that are beneficial, safe, and scientifically rigorous
Lead the vision, strategy, and execution of innovative solutions that leverage our latest models' capabilities for scientific applications
Contribute to thought leadership through publications, conference presentations, and community engagement in the life sciences AI space