AVP - Design Validation & Learning, Biotherapeutics

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. At Lilly, we unite caring with discovery to make life better for people around the world. For more than 25 years, Lilly's Biotechnology Discovery Research (BioTDR) organization has advanced novel antibody and peptide therapeutics from concept through clinical development to market in areas of high unmet medical need. This role is based at the Lilly Biotechnology Center in San Diego, where we are expanding our computational capabilities to build a generative design platform. By bringing together AI/ML, protein engineering, automation, structural biology, and high-throughput experimentation, BioTDR is creating an integrated de novo design platform capable of generating, ranking, and optimizing novel therapeutic antibodies and other biotherapeutics with improved activity, safety, manufacturability, and target coverage. Designs are rapidly evaluated through experimental validation, creating a continuous learning cycle that strengthens both models and discovery outcomes. The program is supported by dedicated capacity on LillyPod, our wholly owned 1,016-GPU NVIDIA Blackwell Ultra SuperPOD. Help us push the de novo protein design frontier beyond binders to molecules that can become medicines, faster. Ready to make an impact? Join us. Primary Responsibilities Our Generative Design platform has three pillars: Generative Foundation Models , Generative Protein Design & Optimization , and Active Learning & Design Validation . The AVP of Design Validation & Learning will define and execute the scientific strategy that determines how Lilly evaluates, validates, and continuously improves its de novo biologics design capabilities. This leader will establish the experimental learning systems, validation standards, benchmarking frameworks, and data generation strategies that transform computational predictions into actionable scientific insights and increasingly powerful design capabilities. This is a founding leadership role. You will build and lead a multidisciplinary team spanning model evaluation, active learning, computational biology, statistical methodology, physics-based modeling, and experimental learning. You will be accountable for ensuring that every experimental outcome strengthens the platform's ability to design better therapeutic molecules over time. Responsibilities Define the scientific strategy for evaluation, validation, and learning. Establish how model performance, design quality, and therapeutic impact will be measured across the platform, ensuring computational progress translates into meaningful experimental outcomes. Define and establish rigorous benchmarks and validation standards. Establish evaluation frameworks that test generalization, prospective design performance, biological relevance, experimental success, and failure modes. Create a culture in which models are challenged against rigorous experimental benchmarks and evidence-based decision making is standard practice. Establish a closed computational-experimental learning system. Partner closely with experimental scientists to connect model development with laboratory testing. Use predefined experimental metrics to evaluate models, uncover failure modes, and continuously improve subsequent generations of models and molecular designs. Set the role of physics-based approaches in the design stack. Decide where simulation, molecular modeling, and energy-based calculations improve model performance and design capability, and define how they should be integrated with ML-based approaches. Active learning and proprietary data strategy. Define the scientific strategy for which experiments should be performed next. Partner with DMTA, automation, protein engineering, and discovery leaders to create proprietary data assets that compound in value for model training, evaluation, and future design cycles. Translate frontier AI research into therapeutic impact. Embed AI capabilities directly into active discovery programs, identify high-value opportunities where computational design can change program outcomes, and establish rigorous measures of impact including novel molecular solutions, hit rates, design precision, experimental efficiency, and advancement of differentiated therapeutic candidates. Establish platform-wide success metrics. Define how model performance, design quality, experimental efficiency, and therapeutic outcomes are measured, tracked, and communicated across the Generative Design Platform. Build and lead a world-class interdisciplinary organization. Recruit, develop, and inspire researchers spanning physics-based modeling, computational biology, model evaluation, active learning, statistics, and data science. Establish the culture, scientific standards, and talent strategy needed for long-term success. Shape the enabling technology ecosystem. Partner with data engineering, MLOps, computational infrastructure, and enterprise technology organizations to ensure researchers have scalable data, training, inference, experimentation, and deployment capabilities while maintaining focus on differentiated scientific innovation. 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. Basic Requirements Ph.D. in computational biology, biophysi