Advisor - Antibody Developability Validation & Benchmarking

Purpose

Advisor/Senior Advisor – Antibody Develop ability Validation & Benchmarking for Lilly Tune Lab (AI-powered federated learning platform). Establish trustworthiness of federated antibody models for triage/go-no-go decisions; contribute to model design with close partnership on architecture, feature design, and uncertainty quantification.

• Build canonical develop ability benchmark suite (aggregation propensity, thermal stability, polyspecificity, self-interaction, viscosity, chemical liabilities, immunogenicity surrogates) and define endpoint evaluation/roll-up for triage.
• Design sequence-aware federated test sets with privacy-preserving, representative splits (germline-, CDR-similarity-, clonotype-based) and held‑out evaluation accounting for antibody data asymmetry.
• Integrate and benchmark against external public resources (SAbDab, OAS, TAP, Jain panel, FLAb, emerging datasets) to quantify generalization gaps vs public‑only baselines.
• Develop cross‑domain validation across IgG/bispecific/fragment formats, expression systems, and partner assay protocols.
• Implement temporal and sequence‑similarity‑aware validation to simulate deployment, detect concept drift, and surface failure modes.
• Partner on validation‑impacting model design choices (uncertainty quantification, calibration, structure‑aware vs sequence‑only, endpoint combination/independence).
• Ensure statistical rigor (multiple testing, hierarchical structures, confidence intervals).
• Build reproducibility MLOps infrastructure (data snapshot/model checkpoint/hyperparameter versioning).
• Perform performance profiling across germline/length regimes/property ranges.
• Integrate validation frameworks with Tune Lab (NVIDIA FLARE) for scalable automated testing.

• PhD in Computational Biology/Bioinformatics/Computational Chemistry/Computer Science/Statistics or related.
• 4+ years post‑PhD in antibody discovery/engineering/develop ability data in biopharma or academic setting.
• Experience analyzing/modeling antibody develop ability assay data (e.g., HIC, AC‑SINS, nanoDSF, polyspecificity panels, viscosity, chemical liabilities).
• Hands‑on antibody numbering tools (ANARCI or equivalent) and knowledge of Kabat/Chothia/IMGT.
• Experience designing ML validation protocols for biological sequence data (similarity‑aware splits, held‑out test design).

• Fine‑tune protein/antibody language models; knowledge of sequence liability motifs.
• Experimental design, statistical validation, hypothesis testing.
• Data engineering, automation; NVIDIA FLARE or similar federated frameworks.
• Antibody structure prediction tools (Alpha Fold‑Multimer, IgFold, ABodyBuilder).
• Familiarity with SAbDab/OAS/TAP/Jain/FLAb; manufacturability funnel.
• Regulatory considerations for AI/ML; uncertainty quantification + calibration.
• PyTorch, ML ecosystem (HF, scikit‑learn, RDKit), MLflow/W&B; publications; scientific rigor; strong technical writing; partner‑facing model cards/reports.

Indianapolis, San Francisco, or Boston; up to 10% travel.

• Anticipated wage: $166,500–$266,200; eligible for company bonus and comprehensive benefits (401(k), medical/dental/vision, life insurance, time off/leave, well‑being).