Advisor - AI/ML Generative Foundation Models for Biotherapeutics

Eli Lilly and Company • San Diego, CA

5h • Onsite

About The Position

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. Advisor - AI/ML Generative Foundation Models for Biotherapeutics 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.

Requirements

• Ph.D. in computer science, mathematics, physics, computational biology, or a related quantitative field, or an M.S. with 3+ years of relevant research experience in machine learning, computational biology, or a related domain.
• Fluency in Python and PyTorch, with hands-on experience training deep learning models on GPUs, including multi-node or distributed runs.

Nice To Haves

• Demonstrated experience applying geometric deep learning, including equivariant networks, diffusion or flow-matching methods, or graph transformers to train structure-based generative models.
• Peer-reviewed publication or equivalent public contribution applying machine learning to molecular or biological data.
• Ability to develop and implement new deep learning methods.
• Consistent record of clean, tested, reproducible research code and version-controlled, shared training pipelines.
• Participation in the open-source community through released code, models, benchmarks, or other widely used contributions.
• Experience working with wet-lab data, including noisy and negative experimental results, and partnering with the scientists designing those experiments.
• Experience mentoring interns or junior scientists, or leading a small technical sub-project.
• Proficiency with antibody or VHH molecules is desired but not required.
• Strong written and verbal communication skills with scientists of diverse expertise and backgrounds.
• Ability to work productively in an interdisciplinary environment spanning computational and experimental sciences.

Responsibilities

• Develop and train protein and antibody foundation models and invent new methods where published approaches fall short.
• Implement architectures, train at scale on dedicated LillyPod capacity, and iterate model training to improve performance with feedback from experimental validation.
• Solve key technical problems within a modeling workstream, contribute novel research ideas, and help translate advances in AI into measurable impact on therapeutic discovery.
• Develop model architectures, implementing, adapting, and where needed inventing approaches for protein design across sequence-structure generation, epitope conditioning, and confidence prediction.
• Contribute novel technical ideas and research directions, helping advance the state of the art in foundation models and generative AI for biotherapeutics.
• Run training at scale, executing training and fine-tuning experiments on LillyPod while tracking configurations, checkpoints, and results for reproducibility.
• Curate training data, preparing internal structure, sequence, and assay data, including processing and interpretation, in partnership with subject matter experts.
• Evaluate and calibrate, building evaluations that connect model outputs to experimental results.
• Diagnose model failures, investigating failure modes, identifying root causes, and developing improvements to model architectures, data strategies, or training approaches.
• Partner cross-functionally, working closely with peers, protein scientists, and automation colleagues to benchmark model performance.
• Communicate results, presenting clearly to both computational and wet-lab audiences and publishing at external conferences and in the scientific community.

Benefits

• company bonus (depending, in part, on company and individual performance)
• company-sponsored 401(k)
• pension
• vacation benefits
• 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
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree