Research
The GCF6 Agentic AI Lead – Protein Design & Molecular Engineering is a senior scientific and technical leader responsible for defining and driving AI-enabled workflows that accelerate protein engineering, structure prediction, molecular design, and related discovery activities.
This role combines deep domain expertise in computational biology and molecular engineering with a strong understanding of emerging AI technologies, including foundation models, scientific AI, and agentic systems.
The leader identifies high-value scientific opportunities, designs AI-assisted workflows, and partners with ML engineers to build reusable agentic capabilities that enhance scientific productivity and decision-making.
This role serves as the primary scientific lead for AI applications in protein engineering and molecular design.
Core Responsibilities
Scientific AI Strategy
Develop and maintain a roadmap for AI-enabled capabilities supporting:
Identify opportunities where AI agents, scientific models, and automation can significantly improve scientific workflows and outcomes.
Agentic Workflow Design
Design AI-assisted workflows that combine:
Define agent responsibilities, decision pathways, tool integration patterns, and human oversight requirements.
Guide development of multi-agent systems that support complex scientific analyses and discovery workflows.
Scientific Leadership
Serve as the primary interface with research scientists and computational biology teams.
Translate scientific challenges into AI opportunities and technical requirements.
Provide scientific oversight for AI-enabled solutions and ensure outputs align with biological principles and research objectives.
Scientific Model Integration
Guide adoption and evaluation of scientific AI technologies including:
Assess scientific utility, limitations, and opportunities for integration into broader workflows.
Collaboration & Delivery
Partner closely with:
Drive prioritization and execution of AI initiatives within the protein engineering and molecular design portfolio.
Core Competencies
Deep expertise in one or more of:
Strong understanding of:
Ability to connect scientific objectives with AI capabilities and practical implementation strategies.
Core Success Measures
Preferred Qualifications
PhD in Computational Biology, Bioinformatics, Structural Biology, Biophysics, Protein Engineering, Computer Science, or related field.
Experience applying AI and machine learning to molecular or biological discovery problems.
Demonstrated leadership in cross-functional scientific initiatives.
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We helped establish the biotechnology industry, and we remain on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today. Our investment in research and development has yielded a robust pipeline that builds on our existing portfolio of medicines to treat cancer, heart disease, osteoporosis, inflammatory diseases and rare diseases.
Amgen is one of 30 companies comprising the Dow Jones Industrial Average®, and part of the Nasdaq-100 Index®. In 2024, Amgen was named one of the “World’s Most Innovative Companies” by Fast Company and one of “America’s Best Large Employers” by Forbes.
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