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

Eli Lilly and Company•San Francisco, IN

1d•Onsite

About The Position

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. Our employees around the world work to discover and bring life-changing medicines to those who need them, 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. Purpose Lilly TuneLab 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 our biotech partners. The Machine Learning Scientist/Sr Scientist, Antibody Property Prediction & Generative Design plays an essential role within the TuneLab 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 developability assessment across the federated network.

Requirements

PhD in Computational Biology, Protein Engineering, Immunology, Biochemistry, or 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

Nice To Haves

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 BioLuminate)
Familiarity with antibody-drug conjugates and bispecific platforms
Experience with federated learning in biological applications
Portfolio mindset balancing innovation with practical developability

Responsibilities

Antibody Property Prediction: Build multi-task learning frameworks specifically 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 maintain proper folding, CDR loop conformations, and epitope recognition.
Developability Optimization: Create models that simultaneously optimize for multiple developability 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.

Benefits

Relocation is provided.
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees.
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location.
The anticipated wage for this position is $151,500 - $244,200
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance).
In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).
Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.

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