Machine Learning Scientist/Sr Scientist - Antibody Property Prediction & Generat

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

Join to apply for the Machine Learning Scientist/Sr Scientist – Antibody Property Prediction & Generative Design role at Eli Lilly and Company.

At Lilly, we unite caring with discovery to make life better for people around the world. Eli Lilly is a global healthcare leader headquartered in Indianapolis, Indiana. We work to discover and bring life‑changing medicines to those who need them.

Purpose

The TuneLab platform is an AI‑powered drug discovery platform that gives biotech companies access to machine‑learning models trained on Lilly’s proprietary pharmaceutical research data. Using federated learning, the platform allows Lilly to build AI models on diverse datasets from across the biotech ecosystem while preserving partner data privacy and competitive advantages. This collaborative approach accelerates drug discovery by continuously improving AI models that benefit Lilly and its biotech partners.

The Machine Learning Scientist/Sr Scientist focuses on antibody and biologic drug development within the TuneLab platform. The 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 developability assessment across the federated network.

Key Responsibilities

• Antibody Property Prediction: Build multi‑task learning frameworks for antibody properties including binding affinity, specificity, stability (thermal, pH, aggregation), immunogenicity, and developability 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 (AlphaFold, ESMFold) with generative approaches to ensure generated antibodies fold properly, preserve CDR loop conformation, and recognise epitopes.
• Developability Optimization: Create models that simultaneously optimise multiple developability criteria (expression yield, solubility, viscosity, post‑translational modifications) essential for manufacturing and formulation.
• Species Cross‑Reactivity: Design antibodies with desired species‑cross reactivity profiles for preclinical development, learning from cross‑species binding data.
• Antibody‑Antigen Modeling: Build models that predict antibody‑antigen interactions, epitope mapping, and paratope design, integrating sequence and structural information.

Basic Qualifications

• PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or a related field from an accredited institution.
• 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.

Additional Preferences

• 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, Schrödinger BioLuminate).
• Familiarity with antibody‑drug conjugates and bispecific platforms.
• Experience with federated learning in biological applications.
• A portfolio mindset balancing innovation with practical developability.

Location: Indianapolis, South San Francisco, or Boston with up to 10% travel. Attendance at key industry conferences is expected. Relocation is provided.

Lilly is a committed Equal Opportunity Employer.

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