Director, Generative Design & Optimization, Biotherapeutics

Eli Lilly and Company • San Diego, CA

1d

About The Position

The Director of Generative Design & Optimization will lead the scientific execution of Lilly’s de novo biotherapeutic design capabilities at the Lilly Biotechnology Center in San Diego. The role transforms foundation-model outputs into experimentally testable therapeutic candidates, overseeing design generation, conditioning, sampling, scoring, optimization, and sequence selection. The director works closely with the Generative Foundation Models and Active Learning & Design Validation teams to define and execute technical strategy for AI-enabled biotherapeutic discovery.

The position leads a team of AI researchers and computational scientists while providing hands-on scientific direction. Its work supports an integrated design platform combining AI/ML, protein engineering, automation, structural biology, and high-throughput experimentation to generate and optimize therapeutic antibodies and other biotherapeutics. The platform uses experimental validation in a continuous learning cycle, with dedicated computing capacity on LillyPod.

Requirements

• Ph.D. in computer science, computational biology, bioengineering, chemistry, physics, or a related STEM discipline with 3 or more years of relevant experience; or an M.S. with 6 or more years; or a B.S. with 8 or more years.
• Track record leading computational or generative molecular design efforts that resulted in experimentally evaluated molecular designs.
• Expertise designing and building training or evaluation datasets for sequence, structure, or molecular design models.
• Strong Python skills and experience with a modern deep learning framework and GPU computing.

Nice To Haves

• Proven record in conditional generation, guided sampling, active learning, Bayesian optimization, multi-objective optimization, quality-diversity methods, or related molecular design approaches.
• Expertise in protein structure analysis and translating structural knowledge into model constraints, optimization objectives, or selection criteria.
• Expertise in sequence-identity-aware and structure-aware data splitting, benchmarking, and leakage assessment.
• Contribution to a de novo binder or antibody design effort that reached experimentally validated designs.
• Experience combining learned and physics-based signals in scoring, ranking, or optimization workflows for molecular designs.
• Experience mentoring scientists or engineers or leading a multi-person technical effort.
• Track record of productive collaboration in multidisciplinary teams spanning computational and experimental sciences.
• Ability to communicate across domains and explain design capabilities, opportunities, and limitations to non-computational colleagues.

Responsibilities

• Lead development of conditioning, guidance, and restraint methods for steering foundation-model outputs toward epitope-specific, structurally plausible antibody and VHH designs.
• Devise iterative optimization layers and sampling strategies that balance exploration and exploitation to improve design quality, experimental success, and discovery efficiency.
• Develop scoring and ranking methods that combine learned and physics-based signals to prioritize designs for binding affinity, functional performance, developability, and other therapeutic properties.
• Establish versioned training, fine-tuning, and evaluation corpora, with documented provenance, structure-aware splits, composition analysis, and leakage controls.
• Leverage multimodal and proprietary biological data to support molecular design.
• Develop and operationalize integrated workflows for conditioned generation, sampling, optimization, scoring, diversity selection, and experimental test nomination across discovery programs.
• Establish metrics connecting computational design quality with experimental outcomes, therapeutic objectives, and downstream portfolio impact.
• Set technical direction and priorities, recruit and develop scientists and engineers, establish technical standards, and deliver committed milestones.
• Collaborate with the Foundation Models and Active Learning & Design Validation teams to benchmark and test pipeline components against experimental evidence and use results to guide subsequent iterations.
• Assess advances in generative biology, molecular optimization, active learning, and AI-enabled therapeutic design, deciding when to build, adapt, or partner.
• Present technical progress, experimental validation, key learnings, and strategic recommendations to scientific leadership and broader R&D stakeholders.
• Contribute to publications, external collaborations, and scientific presentations where appropriate.

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: Director