Our Generative Design platform has three pillars: Generative Foundation Models, Generative Protein Design & Optimization, and Active Learning & Design Validation.
The AVP of Generative Design & Optimization for Biotherapeutics will define and execute the scientific strategy that transforms frontier AI models into a scalable and controllable design engine for therapeutic discovery. This leader will establish Lilly’s long-term capabilities in de novo biologics design, defining how generation, conditioning, sampling, scoring, optimization, and selection work together to produce experimentally testable therapeutic candidates. This is a founding leadership role. You will build and lead a high-performing organization while remaining closely connected to the science, ensuring innovation translates into meaningful portfolio impact. You are accountable for developing a design platform that performs across diverse target classes and evolves as biologics discovery challenges become increasingly complex.
· Advance controllable molecule generation. Translate therapeutic design intent into explicit computational design specifications. Develop approaches for intentional design against defined biological objectives including epitope, molecule format, and therapeutic attributes.
· Define sampling, search, and scoring strategy. Establish approaches that maximize design quality and portfolio impact, determining where increased exploration improves outcomes and where intelligent search provides greater efficiency than brute-force scaling.
· Lead multi-objective optimization. Establish Pareto-based and constraint-based optimization strategies spanning binding affinity, kinetics, specificity, functional activity, developability, immunogenicity, and other therapeutically relevant properties.
· Define data and corpus strategy. Determine how internal and external biological, structural, and experimental data are converted into design-ready assets that accelerate model performance and therapeutic discovery. Training corpus content and sampling strategy will be jointly governed with the Foundation Models and Active Learning & Design Validation teams.
· Establish an end-to-end design pipeline. Build robust workflows spanning epitope definition, conditioned generation, sampling and search, scoring, optimization, diversity selection, and experimental nomination. Ensure workflows are scalable, reproducible, traceable, and adaptable across programs.
· Define success metrics and decision frameworks for generative design performance. Ensure progress is measured against experimental outcomes and therapeutic objectives rather than computational benchmarks alone.
· Build and lead a world-class organization and provide cross-functional leadership. Recruit, develop, and retain exceptional computational scientists and AI researchers. Establish the culture, technical standards, and talent strategy needed for long-term success. Partner closely with biotherapeutics discovery, automation, experimental sciences, and computational leaders across Lilly to maximize portfolio impact.
· Evaluate emerging technologies and external innovation. Stay current with advances in generative AI, molecular design, optimization, active learning, and related technologies. Evaluate external methods, collaborations, and partnerships, and determine when to build, adapt, or partner.
· Represent the organization as a scientific leader. Serve as an internal and external thought leader in AI-enabled biologics discovery, communicating scientific strategy and progress to senior leadership and representing the organization through publications, conferences, collaborations, and engagement with the broader scientific community.