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

Role Summary

The Machine Learning Scientist/Sr Scientist, Antibody Property Prediction & Generative Design drives AI model development for antibody discovery and developability assessment within Lilly TuneLab. The role combines deep expertise in antibody engineering, protein design, and immunology with advanced machine learning techniques to accelerate antibody discovery, optimization, and cross-functional collaboration across a federated network.

Responsibilities

• Antibody Property Prediction: Build multi-task learning frameworks for antibody properties including binding affinity, specificity, stability, immunogenicity, and developability metrics from sequence and structural features.
• Antibody Sequence Generation: Develop generative models for antibody design, including CDR optimization, humanization, and affinity maturation while preserving structural integrity.
• Structure-Aware Design: Integrate structural modeling and prediction with generative approaches to ensure proper folding, CDR loop conformations, and epitope recognition.
• Developability Optimization: Create models that optimize for expression yield, solubility, viscosity, and post-translational modifications for manufacturing and formulation.
• Species Cross-Reactivity: Design antibodies with desired species cross-reactivity profiles for preclinical development using cross-species data.
• Antibody-Antigen Modeling: Develop models for predicting antibody-antigen interactions, epitope mapping, and paratope design using sequence and structural information.

Qualifications

• Required: PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or related field from an accredited college or university
• Required: Minimum of 2 years of experience in antibody or protein therapeutic development within the biopharmaceutical industry
• Required: Strong experience with protein sequence analysis and structural biology
• Required: Proven track record in machine learning applications to biological sequences
• Required: Deep understanding of antibody structure-function relationships and immunology
• Preferred: Experience with immune repertoire sequencing and analysis
• Preferred: Publications on antibody design, protein engineering, or therapeutic development
• Preferred: Expertise in protein language models and transformer architectures
• Preferred: Knowledge of antibody manufacturing and CMC considerations
• Preferred: Experience with display technologies (phage, yeast, mammalian)
• Preferred: Understanding of clinical immunogenicity and prediction methods
• Preferred: Proficiency in protein modeling tools (Rosetta, MOE, Schrodinger BioLuminate)
• Preferred: Familiarity with antibody-drug conjugates and bispecific platforms
• Preferred: Experience with federated learning in biological applications
• Preferred: Portfolio mindset balancing innovation with practical developability

Education

• PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or related field

Additional Requirements

• Location: Indianapolis, IN; South San Francisco, CA; or Boston, MA; up to 10% travel to attend key industry conferences
• Relocation assistance provided