AVP, AI/ML - Generative Foundation Models for 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 Generative Foundation Models for Biotherapeutics will define and execute a scientific strategy for protein foundation models, starting with antibodies and VHHs and expanding across biotherapeutic modalities as the platform matures. This leader will play a central role in establishing Lilly's long-term internal de novo biologics design capabilities and shaping the future of AI-enabled biotherapeutic discovery. This is a founding leadership role. You'll build and lead the organization behind that strategy while remaining closely connected to the science, ensuring technical innovation translates into meaningful impact across Lilly's discovery portfolio. Set scientific vision and technical roadmap for generative AI and protein foundation models, identifying where new computational approaches can change how therapeutic molecules are discovered and optimized. Lead the team to establish and advance structure-based generative models for de novo antibody design. Set pretraining and post-training strategy across self-supervised learning, fine-tuning, transfer learning, reward-guided optimization, and reinforcement learning, and decide how proprietary binding, functional, and developability measurements enter training. Define strategy for confidence estimation, out-of-distribution detection, failure-mode characterization, and applicability-domain assessment against experimental outcomes. Define model scaling and infrastructure strategy across data, compute, model architecture, and training systems. Create learning strategies that leverage proprietary biological data, partnering with data, experimental, and scientific teams on multimodal integration to drive increasingly powerful models. Build and lead a world-class organization of AI researchers, ML infrastructure engineers, and computational scientists, establishing the culture, technical standards, and talent strategy needed for long-term success while attracting and retaining exceptional talent. Evaluate emerging foundation-model architectures, external technologies, and scientific advances to inform platform strategy, partnership opportunities, and technical direction. Drive cross-disciplinary collaboration, pairing AI researchers with antibody engineers, structural biologists, and therapeutic-area experts so domain insight shapes model development. Partner with data engineering, MLOps, and infrastructure teams to keep researchers focused on scientific innovation rather than platform maintenance. Represent the organization as a scientific leader, communicating strategy to senior leadership and engaging the broader scientific community through publications, conferences, and collaborations. Basic Requirements Ph.D. in computer science, mathematics, physics, computational biology, or a related quantitative STEM field, with 5+ years post-Ph.D. research experience (or M.S. with 10+ years), including at least 3 years as a formal supervisor. Track record in generative or predictive modeling for protein structure, sequence design, or molecular interaction, with first-author or equivalent contributions. Experience training deep learning models at scale on multi-node GPU infrastructure, with fluency in Python and PyTorch and the depth to challenge architecture and training decisions directly. Additional Preferences Demonstrated ability to influence at the senior-executive level, including presenting research strategy and compute or headcount requests to decision-makers. Experience leading research in a large, matrixed organization and operating through ambiguity. Depth in equivariant architectures, diffusion or flow matching on structure, and graph transformers, with hands-on work in all-atom generation, side-chain packing, or inverse folding. Experience valida