Advisor, Design Validation & Learning, Biotherapeutics

100 Eli Lilly and Company; San Diego (CA); On-site; USD 168,000 - 268,000; Full time

Job summary

Lilly Biotechnology Center in San Diego seeks a computational biologist to advance a generative design platform for therapeutic antibodies and other biotherapeutics. You will integrate AI/ML with protein engineering, automation, and high-throughput experimentation to improve activity, safety, and manufacturability through a closed-loop learning cycle.

The role requires deep expertise in de novo design evaluation, molecular simulations, and data-driven decision making, with strong collaboration

Qualifications

• PhD in a STEM field or MS/BS with years of experience.
• Experience with protein language models, structure prediction, docking, or molecular scoring.
• Hands-on use of molecular simulation tools and data pipelines.

Responsibilities

• Perform rigorous evaluation and validation analyses translating experimental data into insights.
• Run biophysical and structural analyses to assess stability and developability.
• Build reproducible data pipelines preserving design, sample, and assay metadata.
• Collaborate with protein science and automation teams for informative data-generation.
• Integrate outcomes into training-ready data products and design iterations.
• Communicate findings clearly to computational and experimental teams.

Skills

Data analysis

Scientific communication

Collaboration

Presentation skills

Education

PhD level

MS with 3+ years

BS with 5+ years

Tools

OpenMM

GROMACS

Amber

Rosetta

Schrödinger

Snakemake

Nextflow

Docker

Job description

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

You will perform rigorous evaluation and validation analyses that translate experimental evidence into actionable insights for improving Lilly's de novo design capabilities.

• Evaluation analysis. Assemble held-out datasets, compare computational predictions with experimental outcomes, quantify uncertainty, and identify patterns, limitations, and failure modes that inform subsequent model and design improvements.
• Biophysical calculations. Run molecular simulation, energy-based, and structure-based analyses and evaluate features for stability, aggregation, and developability prediction, determining where these approaches add value alongside ML-based methods.
• Data pipelines. Build reproducible workflows that preserve design, sample, assay, and experimental metadata.
• Experimental partnership. Work with protein science and automation colleagues to plan informative data-generation studies and ensure resulting data are suitable for model evaluation, refinement, and subsequent learning cycles.
• Closed-loop learning. Integrate positive, negative, and ambiguous experimental outcomes into evaluation datasets and training-ready data products, and help translate those findings into recommendations for subsequent model and design iterations.
• Scientific communication. Present findings, uncertainty, model limitations, and experimental evidence clearly to computational and experimental teams, translating results into actionable recommendations for subsequent work.

Basic Requirements

• Ph.D. in computational biology, biophysics, computational chemistry, bioengineering, chemical engineering, physics, statistics, or a related STEM discipline; or an M.S. with 3+ years; or a B.S. with 5+ years.

Additional Preferences

• Hands-on use of OpenMM, GROMACS, Amber, Rosetta, Schrödinger, or related simulation tools.
• Understanding of protein structure and biophysics and of binding, expression, and stability measurements.
• Experience with protein language models, structure prediction, docking, or molecular scoring.
• Experience with binding and biophysical measurements such as SPR, BLI, thermostability, expression, deep mutational scanning, or other high-throughput biological datasets.
• Experience with antibody or VHH developability.
• Experience with workflow tools such as Snakemake, Nextflow, containers, or scheduled cluster/GPU computing.
• Direct wet-lab experience or close collaboration with experimental scientists.
• Strong written and verbal communication skills, including the ability to communicate quantitative findings, uncertainty, and model limitations to computational and experimental scientists.

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 (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.