Our Generative Design platform has three pillars: Generative Foundation Models, Generative Protein Design & Optimization, and Active Learning & Design Validation. The Director of Generative Foundation Models will lead the development of protein and antibody foundation models and validate their performance against experimental outcomes, not just published baselines. You will be accountable for the program’s generative model families end to end, across architecture choice, training strategy, and validation, and co-define the program’s technical agenda with leadership. You will lead a team of AI researchers and ML infrastructure engineers while staying hands-on and giving direct technical guidance on every model in your workstream. This is an opportunity for a scientific leader who wants to build and scale technologies that directly shape therapeutic molecule discovery.
· Lead generative AI and protein foundation model development, driving state-of-the-art machine learning approaches for representation learning, generative protein design, sequence-structure-function modeling, and multimodal biological learning.
· Set training strategy, developing the pre-training and fine-tuning approach for your models across architecture choice, model family, training curriculum, and evaluation.
· Devise training infrastructure, designing and steering the training lifecycle and scaling approach against LillyPod capacity, accountable for reproducibility, checkpointing, and throughput, with the ML infrastructure engineers on your team.
· Direct confidence and calibration work, treating it as a first-class research objective and reducing the gap between model-ranked and experimentally validated design performance.
· Drive model improvement, resolving underperforming models to root cause across architecture, data, and execution, and making timely decisions to retrain, pivot, or discontinue.
· Drive the advancement of foundation model portfolio, establishing success metrics and ensuring progress translates into measurable experimental outcomes.
· Leverage multimodal and proprietary biological data, creating differentiated learning advantages by bringing proprietary sequence, structure, binding, functional, developability, and other biological measurements into model development.
· Lead a high-performing team and provide scientific and technical leadership, recruiting and developing AI researchers and engineers on your team and providing sustained technical mentorship. Guide model architecture choices, experimental design, technical prioritization, and research direction while remaining sufficiently close to the science and technology to challenge assumptions and identify new opportunities.
· Evaluate and incorporate external innovation, staying current with rapidly evolving advances in foundation models, generative AI, protein design, structural modeling, and related technologies. Evaluate external methods and collaborations and determine when to build, adapt, or partner.
· Communicate scientific strategy and impact, presenting 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.