Generative Design Scientist for Antibody Discovery

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

Job summary

Eli Lilly and Company is expanding its de novo design capabilities at the San Diego Lilly Biotechnology Center. You will join a multidisciplinary team to build a design engine for antibody and VHH discovery using generative protein models, conditioning, sampling, and scoring methods.

The role emphasizes hands-on research, collaboration with model researchers, and translating AI advances into therapeutic discovery outcomes, with significant emphasis on data curation and experimental validation

Qualifications

• 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.

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.
• Validation support: prepare design sets for experimental testing with the Active Learning & Design Validation team, track outcomes, and feed results back into method comparisons.
• Collaboration: partner with various science teams to improve design performance and accelerate discovery outcomes.
• Scientific communication: present findings clearly to computational and experimental teams and contribute to publications and presentations where appropriate.

Skills

ML in life sciences

Python programming

GPU computing

Education

PhD in CS or related field

MS with 2+ years experience

BS with 3+ years experience

Tools

TensorFlow / PyTorch

Job description

Eli Lilly and Company is expanding its de novo design capabilities at the San Diego Lilly Biotechnology Center. You will join a multidisciplinary team to build a design engine for antibody and VHH discovery using generative protein models, conditioning, sampling, and scoring methods.

The role emphasizes hands-on research, collaboration with model researchers, and translating AI advances into therapeutic discovery outcomes, with significant emphasis on data curation and experimental validation