Advisor - Generative Design & Optimization, 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. 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.

Primary Responsibilities

• Conditioning and sampling. Implement, test, and compare conditioning, guidance, restraint, and sampling methods that steer foundation-model output toward target-specific antibody and VHH designs.
• Advance design methods. Develop, evaluate, and refine novel approaches for molecular generation, optimization, and candidate selection when existing methods fall short of design objectives.
• Optimization. Implement and evaluate iterative refinement and optimization methods, and determine which approaches most effectively improve design quality relative to computational and experimental cost.
• Scoring and ranking. Apply, evaluate, and improve scoring methods that combine learned and physics-based signals to rank designs against affinity, epitope, diversity, developability, and other therapeutically relevant criteria.
• Dataset curation and evaluation. Prepare, quality-check, version, and document sequence, structure, and assay datasets for design and evaluation. Maintain held-out datasets, perform leakage checks, and compare design methods using fit-for-purpose metrics.
• Validation support. Prepare design sets for experimental testing with the Active Learning & Design Validation team, track outcomes, and feed results back into method comparisons and future design iterations.
• Collaboration. Partner with foundation model researchers, design validation scientists, data scientists, protein scientists, structural biologists, and automation colleagues to improve design performance and accelerate discovery outcomes.
• Scientific communication. Present findings clearly to computational and experimental teams and contribute to publications, external presentations, and scientific communications where appropriate.

Basic Requirements

• Ph.D. in computer science, computational biology, bioengineering, chemistry, physics, or a related STEM field; or an M.S. with 2+ years of relevant experience; or a B.S. with 3+ years of relevant experience.
• Hands‑on experience applying machine learning to sequence, structure, molecular, biological, or other scientific data.
• Proficiency in Python and a modern deep learning framework, with experience using GPU computing.

Additional Preferences

• Hands‑on experience with active learning, Bayesian optimization, multi‑objective optimization, quality‑diversity methods, or related optimization approaches.
• Experience curating structural, sequence, or assay datasets, including held‑out dataset creation, structure‑aware evaluation, and leakage assessment.
• Experience with generative models for proteins or other molecules, including diffusion models, flow‑matching, autoregressive sequence models, or related approaches.
• Exposure to physics‑based or structure‑based scoring methods, such as Rosetta, FoldX, docking methodologies, or energy‑based calculations.
• Working knowledge of protein structures, antibodies, VHHs, or related therapeutic modalities.
• Experience working with experimental scientists and biological assay data.
• Strong written and verbal communication skills, including the ability to explain model behavior, design tradeoffs, and limitations to non‑computational colleagues.
• Ability to work productively in a multidisciplinary environment spanning computational science, protein engineering, structural biology, automation, and experimental biology.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions.

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 (View email address on click.appcast.io), 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).

Benefits

• 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
• 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 At Lilly we strive to ensure our employees are part of a team that cares about them and our shared purpose of making life better for those around the world. How do we do this? We continue to look for ways to include, innovate, accelerate and deliver while maintaining integrity, excellence and respect for people. We hope that you seek to join us on our journey as we create medicine and deliver improved outcomes for patients across the globe!

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