Machine Learning Scientist​/Sr Scientist - Antibody Property Prediction & Generative Design

Machine Learning Scientist/Sr Scientist - Antibody Property Prediction & Generative Design

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana, and our employees work to discover and bring life‑changing medicines, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first.

We’re looking for people who are determined to make life better for people around the world.

Lilly Tune Lab is an AI‑powered drug discovery platform that provides biotech companies with access to machine learning models trained on Lilly's extensive proprietary pharmaceutical research data. Through federated learning, the platform enables Lilly to build models on broad, diverse datasets from across the biotech ecosystem while preserving partner data privacy and competitive advantages. This collaborative approach accelerates drug discovery by creating continuously improving AI models that benefit both Lilly and its biotech partners.

The Machine Learning Scientist/Sr Scientist, Antibody Property Prediction & Generative Design plays an essential role within the Tune Lab platform, specializing in antibody and biologic drug development. This position requires deep expertise in antibody engineering, protein design, and immunology, combined with advanced machine learning capabilities in sequence modeling and structure prediction. The role will drive the development of AI models that accelerate antibody discovery, optimization, and develop ability assessment across the federated network.

• Antibody Property Prediction:
  Build multi‑task learning frameworks for antibody properties including binding affinity, specificity, stability (thermal, pH, aggregation), immunogenicity, and develop ability metrics from sequence and structural features.
• Antibody Sequence Generation:
  Develop and implement generative models (transformers, diffusion models, evolutionary models) for antibody design, including CDR optimization, humanization, and affinity maturation while maintaining structural integrity.
• Structure‑Aware Design:
  Integrate structural modeling and prediction (Alpha Fold, ESMFold) with generative approaches to ensure generated antibodies maintain proper folding, CDR loop conformations, and epitope recognition.
• Develop ability Optimization:
  Create models that simultaneously optimize for multiple develop ability criteria including expression yield, solubility, viscosity, and post‑translational modifications, crucial for manufacturing and formulation.
• Species Cross‑Reactivity:
  Develop approaches to design antibodies with desired species cross‑reactivity profiles for preclinical development, learning from cross‑species binding data.
• Antibody‑Antigen Modeling:
  Create models for predicting antibody‑antigen interactions, epitope mapping, and paratope design, incorporating both sequence and structural information.

• PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or a related field from an accredited college or university.
• Minimum of 2 years of experience in antibody or protein therapeutic development within the biopharmaceutical industry.
• Strong experience with protein sequence analysis and structural biology.
• Proven track record in machine learning applications to biological sequences.
• Deep understanding of antibody structure‑function relationships and immunology.

• Experience with immune repertoire sequencing and analysis.
• Publications on antibody design, protein engineering, or therapeutic development.
• Expertise in protein language models and transformer architectures.
• Knowledge of antibody manufacturing and CMC considerations.
• Experience with display technologies (phage, yeast, mammalian).
• Understanding of clinical immunogenicity and prediction methods.
• Proficiency in protein modeling tools (Rosetta, MOE, Schrodinger Bio Luminate).
• Familiarity with antibody‑drug conjugates and bispecific platforms.
• Experience with federated learning in…