AVP, Generative Design & Optimization, Biotherapeutics

Lilly • San Diego, CA

10d • $267,000 - $391,600 • Onsite

About The Position

Lilly’s Biotechnology Discovery Research organization advances antibody and peptide therapeutics from concept through clinical development to market. The role is based at the Lilly Biotechnology Center in San Diego, where Lilly is expanding computational capabilities to build an integrated de novo design platform combining AI/ML, protein engineering, automation, structural biology, and high-throughput experimentation. The platform generates, ranks, and optimizes therapeutic antibodies and other biotherapeutics, with designs evaluated experimentally in a continuous learning cycle. The program has dedicated capacity on LillyPod, a wholly owned 1,016-GPU NVIDIA Blackwell Ultra SuperPOD.

The AVP will define and execute the scientific strategy for transforming frontier AI models into a scalable, controllable design engine for therapeutic discovery. This founding leadership role establishes long-term de novo biologics design capabilities, including how generation, conditioning, sampling, scoring, optimization, and selection combine to produce testable therapeutic candidates across diverse target classes. The leader will build and lead a high-performing organization while remaining closely connected to the science and translating innovation into portfolio impact.

Requirements

• Ph.D. in computer science, computational biology, bioengineering, chemistry, physics, or a related STEM field, with at least 5 years of post-Ph.D. research experience; alternatively, an M.S. with at least 10 years of experience.
• At least 3 years of experience leading scientists or engineers as a formal people manager, including building or scaling a team.
• Demonstrated experience leading computational or generative molecular design campaigns that resulted in experimentally evaluated molecular designs.
• Demonstrated experience setting data strategy for molecular design models, including sourcing, integrating, and governing heterogeneous internal and external sequence, structure, and assay data.
• Proficiency in Python and a modern deep learning framework, with working knowledge of GPU-based computing.

Nice To Haves

• Expertise in conditional generation, guided sampling, active learning, Bayesian optimization, multi-objective optimization, quality-diversity methods, or related molecular-design approaches.
• Expertise integrating learned and physics-based scoring into molecular-design workflows.
• Strong structural biology judgment, including translating epitope and interface information into model constraints, optimization objectives, or selection criteria.
• Expertise in antibody, VHH, peptide, or other therapeutic protein design.
• Experience establishing rigorous design, optimization, benchmarking, or evaluation frameworks adopted across research organizations or discovery programs.
• Experience collaborating effectively across computational and experimental disciplines.
• Excellent communication skills, including explaining computational concepts to non-computational scientists, influencing senior scientific leaders, and driving cross-functional alignment.

Responsibilities

• Translate therapeutic design intent into explicit computational specifications and develop methods for intentional design against biological objectives, including epitope, molecule format, and therapeutic attributes.
• Establish sampling, search, and scoring strategies that maximize design quality and portfolio impact, balancing exploration, intelligent search, and brute-force scaling.
• Lead multi-objective optimization strategies spanning binding affinity, kinetics, specificity, functional activity, developability, immunogenicity, and other therapeutically relevant properties.
• Define data and corpus strategy, converting internal and external biological, structural, and experimental data into design-ready assets; govern training corpus content and sampling jointly with Foundation Models and Active Learning & Design Validation teams.
• Build end-to-end design workflows spanning epitope definition, conditioned generation, sampling and search, scoring, optimization, diversity selection, and experimental nomination; ensure workflows are scalable, reproducible, traceable, and adaptable.
• Define success metrics and decision frameworks for generative design, measuring progress against experimental outcomes and therapeutic objectives rather than computational benchmarks alone.
• Build and lead a high-performing organization; recruit, develop, and retain computational scientists and AI researchers, and establish the culture, technical standards, and talent strategy.
• Partner with biotherapeutics discovery, automation, experimental sciences, and computational leaders across Lilly to maximize portfolio impact.
• Evaluate emerging technologies, external methods, collaborations, and partnerships in generative AI, molecular design, optimization, and active learning, and determine when to build, adapt, or partner.
• Represent the organization as an internal and external scientific leader, communicating strategy and progress to senior leadership and through publications, conferences, collaborations, and scientific-community engagement.

Benefits

• Company bonus, depending in part on company and individual performance.
• Eligibility to participate in a company-sponsored 401(k).
• Pension.
• Vacation benefits.
• Eligibility for medical, dental, vision, and prescription drug benefits.
• Flexible benefits, including healthcare and/or dependent day care flexible spending accounts.
• Life insurance and death benefits.
• Certain time-off and leave-of-absence benefits.
• Well-being benefits, including an employee assistance program, fitness benefits, and employee clubs and activities.

What This Job Offers

• Job Type: Full-time
• Career Level: Executive
• Education Level: Ph.D. or professional degree