Agentic AI Lead – Protein Design & Molecular Engineering
About this opportunity
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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Job details
How this role compares
Computed from every other active Information Technology role in our database, not just this employer's listings.
We currently track 421 comparable Information Technology roles across 40 biopharma companies.
Salary context
49 of 421 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
Top locations among the 421 comparable roles
+ 15 more countries
Seniority mix
421 of 421 peers have a known seniority level
Therapeutic area mix
1 of 421 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
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How we calculate "similar"
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Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.