Career CategoryResearchJob DescriptionPosition OverviewThe 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 ResponsibilitiesScientific AI StrategyDevelop and maintain a roadmap for AI-enabled capabilities supporting:Protein engineeringStructure predictionProtein designMotif discoveryProtein-ligand interactionsSequence-function analysisMolecular optimizationIdentify opportunities where AI agents, scientific models, and automation can significantly improve scientific workflows and outcomes.Agentic Workflow DesignDesign AI-assisted workflows that combine:Scientific reasoningFoundation modelsProtein language modelsStructure prediction systemsComputational biology toolsInternal and external knowledge sourcesDefine 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 LeadershipServe 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 IntegrationGuide adoption and evaluation of scientific AI technologies including:Protein language modelsStructure prediction modelsGenerative protein design approachesMolecular foundation modelsEmerging computational biology platformsAssess scientific utility, limitations, and opportunities for integration into broader workflows.Collaboration & DeliveryPartner closely with:ML engineersData engineering teamsPlatform teamsResearch scientistsExternal collaboratorsDrive prioritization and execution of AI initiatives within the protein engineering and molecular design portfolio.Core CompetenciesDeep expertise in one or more of:Computational biologyProtein engineeringStructural biologyMolecular modelingProtein designStrong understanding of:Foundation modelsScientific AIAgentic AI systemsScientific workflow automationAbility to connect scientific objectives with AI capabilities and practical implementation strategies.Core Success MeasuresAdoption of AI-enabled workflows by scientific teamsScientific impact of deployed solutionsReusability of agentic capabilities across programsAcceleration of scientific discovery workflowsEffective collaboration across research and engineering organizationsPreferred QualificationsPhD 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..