Advisor - AI/ML Generative Foundation Models for Biotherapeutics

Lilly • San Diego, CA

7d • $168,000 - $268,400 • Onsite

About The Position

Lilly’s Biotechnology Discovery Research organization develops antibody and peptide therapeutics from concept through clinical development and market. This role is based at the Lilly Biotechnology Center in San Diego and supports an integrated de novo design platform combining AI/ML, protein engineering, automation, structural biology, and high-throughput experimentation. The platform generates, ranks, and optimizes therapeutic antibodies and other biotherapeutics, with experimental validation feeding back into model development. The program has dedicated computing capacity on LillyPod, a company-owned 1,016-GPU NVIDIA Blackwell Ultra SuperPOD.

As a hands-on researcher embedded in a multidisciplinary team, you will develop and train protein and antibody foundation models, implement and adapt architectures, and invent methods when existing approaches are insufficient. You will run and refine training at scale, use experimental feedback to improve model performance, solve technical problems within a modeling workstream, and help translate AI advances into measurable impact on therapeutic discovery. You will also collaborate with computational and experimental colleagues and communicate findings to scientific audiences.

Requirements

• Ph.D. in computer science, mathematics, physics, computational biology, or a related quantitative field; alternatively, an M.S. with 3+ years of relevant research experience in machine learning, computational biology, or a related domain.
• Fluency in Python and PyTorch.
• 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.
• A consistent record of clean, tested, reproducible research code and version-controlled, shared training pipelines.
• Open-source community participation 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 model architectures for protein design, including sequence-structure generation, epitope conditioning, and confidence prediction; implement, adapt, or invent approaches as needed.
• Contribute novel technical ideas and research directions for foundation models and generative AI in biotherapeutics.
• Run model training and fine-tuning experiments at scale on LillyPod, tracking configurations, checkpoints, and results for reproducibility.
• Curate and prepare internal structure, sequence, and assay data, including processing and interpretation in partnership with subject matter experts.
• Build evaluations and calibrate models by connecting model outputs to experimental results.
• Investigate model failure modes, identify root causes, and develop improvements to architectures, data strategies, or training approaches.
• Work with peers, protein scientists, and automation colleagues to benchmark model performance.
• Present results to computational and wet-lab audiences and publish at external conferences and in the scientific community.

Benefits

• Company bonus, depending in part on company and individual performance.
• Eligibility to participate in a company-sponsored 401(k).
• Pension.
• Vacation benefits.
• Eligibility for medical, dental, vision, and prescription drug benefits.
• Flexible benefits, including 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, including an 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