Advisor - Antibody Developability Validation & Benchmarking

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.

At Lilly, we serve an extraordinary purpose. We make a difference for people around the globe by discovering, developing and delivering medicines that help them live longer, healthier, more active lives. Not only do we deliver breakthrough medications, but you also can count on us to develop creative solutions to support communities through philanthropy and volunteerism.

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. Antibody develop ability prediction is a core workstream within Tune Lab—covering aggregation, self‑association, polyspecificity, thermal stability, viscosity, and chemical liabilities—that gates progression from discovery into lead optimization, cell line development, and formulation.

The Advisor/Senior Advisor – Antibody Develop ability Validation & Benchmarking plays an essential role in establishing whether Tune Lab’s federated antibody models can be trusted to triage real candidates. The person in this seat must understand, at depth, how antibodies are characterized, what makes a sequence developable or not, and how predictions from a federated model translate into go/no‑go decisions in a discovery pipeline.

This is a validation‑led role that contributes to model design choices. The person will partner closely with antibody modeling scientists on architecture, feature design, and uncertainty quantification—not just downstream of them.

Build the canonical benchmark suite covering the full develop ability portfolio—aggregation propensity (AC‑SINS, SMAC, CIC), thermal stability (nano

DSF/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 develop ability—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,…