Agentic AI Lead – Protein Design & Molecular Engineering

Career Category

Research

Job Description

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.
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