Advisor, Design Validation & Learning, 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.

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 Lilly Pod, 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.

• 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 develop ability 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.

• 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.

• Proficiency in Python and the scientific computing stack, version control, and reproducible analysis.

• Working knowledge of statistics, uncertainty quantification, regression, agreement metrics, calibration, and experimental design.

• Hands-on use of OpenMM, GROMACS, Amber, Rosetta, Schrödinger, or related simulation tools.

• Understanding of protein structure…