Design Validation & Learning Scientist – De Novo Design

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

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