Agentic AI Lead – Protein Design & Molecular Engineering

Position Overview

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:

• Protein engineering
• Structure prediction
• Protein design
• Motif discovery
• Protein-ligand interactions
• Sequence-function analysis
• Molecular optimization

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:

• Scientific reasoning
• Foundation models
• Protein language models
• Structure prediction systems
• Computational biology tools
• Internal and external knowledge sources

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:

• Protein language models
• Structure prediction models
• Generative protein design approaches
• Molecular foundation models
• Emerging computational biology platforms

Assess scientific utility, limitations, and opportunities for integration into broader workflows.

Collaboration & Delivery

Partner closely with:

• ML engineers
• Data engineering teams
• Platform teams
• Research scientists
• External collaborators

Drive prioritization and execution of AI initiatives within the protein engineering and molecular design portfolio.

Core Competencies

Deep expertise in one or more of:

• Computational biology
• Protein engineering
• Structural biology
• Molecular modeling
• Protein design

Strong understanding of:

• Foundation models
• Scientific AI
• Agentic AI systems
• Scientific workflow automation

Ability to connect scientific objectives with AI capabilities and practical implementation strategies.

Core Success Measures

• Adoption of AI-enabled workflows by scientific teams
• Scientific impact of deployed solutions
• Reusability of agentic capabilities across programs
• Acceleration of scientific discovery workflows
• Effective collaboration across research and engineering organizations

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.