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. 3131Set 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. 3131Lead the team to establish and advance structure-based generative models for de novo antibody design. 3131Set 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. 3131Define strategy for confidence estimation, out-of-distribution detection, failure-mode characterization, and applicability-domain assessment against experimental outcomes. 3131Define model scaling and infrastructure strategy across data, compute, model architecture, and training systems. 3131Create learning strategies that leverage proprietary biological data, partnering with data, experimental, and scientific teams on multimodal integration to drive increasingly powerful models. 3131Build 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. 3131Evaluate emerging foundation-model architectures, external technologies, and scientific advances to inform platform strategy, partnership opportunities, and technical direction. 3131Drive cross-disciplinary collaboration, pairing AI researchers with antibody engineers, structural biologists, and therapeutic-area experts so domain insight shapes model development. 3131Partner with data engineering, MLOps, and infrastructure teams to keep researchers focused on scientific innovation rather than platform maintenance. 3131Represent the organization as a scientific leader, communicating strategy to senior leadership and engaging the broader scientific community through publications, conferences, and collaborations. Basic Requirements 3131Ph.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. 3131Track record in generative or predictive modeling for protein structure, sequence design, or molecular interaction, with first-author or equivalent contributions. 3131Experience 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 3131Demonstrated ability to influence at the senior-executive level, including presenting research strategy and compute or headcount requests to decision-makers. 3131Experience leading research in a large, matrixed organization and operating through ambiguity. 3131Depth 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. 3131Experience validating uncertainty quantification against experimental outcomes, not just held-out structures. 3131Track record of leading a de novo binder or antibody design effort into a discovery portfolio. Antibody or VHH experience is a plus but not required. 3131Experience improving model efficiency, and a released method or model other groups now benchmark against. 3131Track record recruiting senior AI research talent and building a research team from scratch, including first hires and technical standards. 3131Experience owning a large GPU allocation or shared compute platform, and working in a co-innovation or strategic partnership with a compute, platform, or lab-automation partner. Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is $267,000 - $391,600 Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.