Advisor - Antibody Developability Validation & Benchmarking

Eli Lilly and Company • Boston, MA

1d • $166,500 - $266,200 • Hybrid

About The Position

The Advisor/Senior Advisor - Antibody Developability Validation & Benchmarking plays an essential role in establishing whether Lilly's AI-powered drug discovery platform, TuneLab, can be trusted to triage real candidates. This role requires a deep understanding of antibody characterization, developability factors, and how model predictions translate into discovery pipeline decisions. It is a validation-led role that contributes to model design choices, partnering closely with antibody modeling scientists on architecture, feature design, and uncertainty quantification.

Requirements

• PhD in Computational Biology, Bioinformatics, Computational Chemistry, Computer Science, Statistics, or related field from an accredited college or university
• Minimum of 4 years of post-PhD experience working with antibody discovery, engineering, or developability data in a biopharmaceutical or related setting
• Demonstrated experience analyzing or modeling data from antibody developability assays (e.g., HIC, AC-SINS, nanoDSF, polyspecificity panels, viscosity, chemical liabilities), evidenced by publications, project work, or thesis
• Hands-on experience with antibody numbering tools (ANARCI or equivalent) and working knowledge of Kabat, Chothia, and IMGT numbering schemes
• Demonstrated experience designing ML validation protocols for biological sequence data, including sequence-similarity-aware splits and held-out test design

Nice To Haves

• Experience fine-tuning protein or antibody language models (e.g., ESM-2, AbLang, IgBERT, AntiBERTa) for property prediction tasks, including self-supervised pretraining on OAS and fine-tuning strategies for low-data developability endpoints
• Working knowledge of sequence liability motifs (Asp isomerization, Met oxidation, deamidation, glycosylation sites in CDRs)
• Strong foundation in experimental design, statistical validation, and hypothesis testing
• Proficiency in data engineering, pipeline development, and automation
• Experience with NVIDIA FLARE or comparable federated learning frameworks (Flower, OpenFL, PySyft)
• Working knowledge of antibody structure prediction tools (AlphaFold-Multimer, IgFold, ABodyBuilder) and how their outputs feed downstream developability models
• Familiarity with public antibody resources — SAbDab, OAS, TAP, Jain panel, FLAb
• Understanding of the manufacturability funnel from discovery through CLD and formulation, and which developability properties gate which stage
• Knowledge of regulatory considerations for AI/ML in pharmaceutical development
• Experience with uncertainty quantification methods (conformal prediction, Bayesian approaches, ensemble disagreement) and calibration assessment
• Proficiency in PyTorch and the modern ML ecosystem (Hugging Face, scikit-learn, RDKit)
• Experience with experiment tracking and model registry tools (MLflow, Weights & Biases)
• Publications on antibody developability prediction, model validation, benchmarking, or reproducibility
• Exceptional attention to detail and commitment to scientific rigor
• Strong technical writing skills for partner-facing model cards and validation reports
• Portfolio mindset balancing rigorous validation with rapid deployment for partner value

Responsibilities

• Build the canonical benchmark suite covering the full developability portfolio — aggregation propensity (AC-SINS, SMAC, CIC), thermal stability (nanoDSF/DSF), polyspecificity (BVP-ELISA, Heparin RT, PSR), self-interaction, viscosity, chemical liabilities (deamidation, isomerization, oxidation, N-glycosylation in CDRs), and immunogenicity surrogates.
• Define which endpoints are evaluated jointly versus independently and how multi-endpoint reliability rolls up to a triage decision.
• Architect privacy-preserving protocols for constructing representative test sets across distributed partner datasets, with splitting strategies appropriate to antibody data — germline-based, CDR-similarity-based, and clonotype-based splits that genuinely test generalization rather than near-duplicate memorization.
• Account for the structural asymmetry of antibody data (many sequences with shallow characterization, few sequences with deep characterization) when designing held-out evaluation sets.
• Systematically benchmark federated antibody models against established external resources — SAbDab, OAS, TAP, the Jain et al. clinical-stage antibody panel, FLAb, and equivalent emerging datasets — to characterize generalization gaps and quantify where federated training delivers measurable lift over public-only baselines.
• Develop validation strategies that assess model generalization across modalities and formats relevant to antibody developability — IgG vs. bispecific vs. fragment formats, different expression systems, different assay protocols across partners — while respecting partner data boundaries.
• Implement temporal-split and sequence-similarity-aware validation protocols that simulate prospective deployment, detect concept drift as partner data accumulates, and surface systematic failure modes across CDR length distributions, germline families, and physicochemical regimes.
• Work alongside antibody modeling scientists on architectural and feature choices that have direct validation implications — uncertainty quantification approaches, calibration strategies, structure-aware vs. sequence-only representations, and how predictions from different endpoints should be combined or kept independent.
• Design statistically powered validation studies that account for multiple testing across endpoints, hierarchical structure in antibody data (sequences clustered by germline, project, partner), and non-independent observations.
• Provide honest confidence intervals on reported model performance.
• Build robust MLOps pipelines ensuring complete reproducibility of federated experiments, including versioning of data snapshots, model checkpoints, and hyperparameter configurations.
• Develop comprehensive performance profiling across germline families, CDR length regimes, framework variants, and property ranges, identifying systematic biases and failure modes that should be communicated to partners.
• Collaborate with engineering teams to integrate validation frameworks with the TuneLab federated learning platform built on NVIDIA FLARE, ensuring scalable and automated testing across the partner network.

Benefits

• company bonus
• company-sponsored 401(k)
• pension
• vacation benefits
• 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
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree