Advisor - 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.

Our protein AI/ML models currently helps design, select, and optimize antibody and peptide leads, with measurable impact on portfolio programs. We are now expanding the computational team to build a generative design platform linking de novo design directly to the biology we want, delivering molecules with better activity, safety, and manufacturability. Targets are chosen by therapeutic need, including difficult classes like GPCRs, and validated designs are advanced directly into Lilly's portfolio.

This role is based at the Lilly Biotechnology Center in San Diego, where computational science, protein engineering, automation, and biology sit in one building. Designs are made and tested here on a fast cycle, and every result, including the negatives, feeds closed-loop learning. You'll train at scale from day one. The program has dedicated capacity on LillyPod, our wholly owned 1,016-GPU NVIDIA Blackwell Ultra SuperPOD. Compute isn't the constraint here, talent is.

Primary Responsibilities

You will train protein and antibody foundation models and invent the methods where published approaches fall short. Working inside a modeling workstream, you will implement architectures, train at scale on dedicated LillyPod capacity, and iterate model training to improve performance with feedback from experimental validation. This is a hands-on research role embedded in a multidisciplinary team, with mentorship, career development support, and room to take on greater technical ownership over time.

- Develop model architectures, implementing, adapting, and where needed inventing approaches for protein design across sequence-structure generation, epitope conditioning, and confidence prediction.
- 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, not only held-out metrics.
- Diagnose model failures, investigating failure modes, forming hypotheses for the root cause, and proposing concrete fixes to architecture, data, or training setup.
- 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 community.

Basic Requirements

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

Additional Preferences

- Demonstrated experience applying geometric deep learning, e.g. equivariant networks, diffusion or flow-matching, or graph transformers, to structure prediction, sequence design, or protein language 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, not only apply existing ones.
- Consistent record of clean, tested, reproducible research code and version-controlled, shared training pipelines.
- Participation in the open-source community — released code, models, or benchmarks others use.
- Experience working with wet-lab data, including noisy and negative results, and the scientists designing the 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 with scientists of diverse expertise and backgrounds.
- Ability to work productively in an interdisciplinary environment spanning dry and wet lab.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation ) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL).

Actual compensation will depend on a candidate’s education, experience, skills, and geographic location.  The anticipated wage for this position is
$168,000 - $268,400

###

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.

#WeAreLilly