Advisor - Antibody Developability Validation & Benchmarking
Eli Lilly and Company
August 08, 2026
On-site
San Francisco, CA
Clinical Research and Development
Responsibilities
- Build a canonical antibody developability benchmark suite (aggregation propensity, thermal stability, polyspecificity, self-interaction, viscosity, chemical liabilities, immunogenicity surrogates) and define multi-endpoint triage rollups.
- Design sequence-aware privacy-preserving federated test sets (germline-, CDR-similarity-, and clonotype-based splits) and validation held-out evaluation sets.
- Benchmark federated models vs public/external resources (SAbDab, OAS, TAP, Jain panel, FLAb, emerging datasets) and quantify lift.
- Develop cross-domain validation (formats/modalities, expression systems, partner assay protocol differences) and temporal/concept-drift-aware validation.
- Partner on model design choices impacting validation (architecture, feature design, uncertainty quantification, calibration, endpoint combination).
- Ensure statistical rigor, reproducibility (MLOps/data/model/versioning), performance profiling, and platform integration with TuneLab (NVIDIA FLARE).
Qualifications (Required)
- PhD in Computational Biology/Bioinformatics/Computational Chemistry/CS/Statistics or related.
- 4+ years post-PhD antibody discovery/engineering/developability experience.
- Experience analyzing/modeling antibody developability assay data (e.g., HIC, AC-SINS, nanoDSF, polyspecificity panels, viscosity, chemical liabilities).
- Antibody numbering tools (ANARCI or equivalent) and Kabat/Chothia/IMGT knowledge.
- ML validation protocol design for biological sequence data (similarity-aware splits, held-out test design).
Preferences (Preferred)
- Protein/antibody language model fine-tuning (e.g., ESM-2, AbLang, IgBERT, AntiBERTa), liability motif knowledge, uncertainty quantification/calibration.
- NVIDIA FLARE/federated frameworks; structure prediction tools; PyTorch + ML ecosystem; MLflow/W&B; relevant publications; strong technical writing.
Location/Travel
- Indianapolis, San Francisco, or Boston; up to 10% travel.
Compensation & Benefits
- Anticipated wage: $166,500–$266,200; potential company bonus; eligible for 401(k), healthcare, life insurance, time off/leave, and well-being benefits.
- Build a canonical antibody developability benchmark suite (aggregation propensity, thermal stability, polyspecificity, self-interaction, viscosity, chemical liabilities, immunogenicity surrogates) and define multi-endpoint triage rollups.
- Design sequence-aware privacy-preserving federated test sets (germline-, CDR-similarity-, and clonotype-based splits) and validation held-out evaluation sets.
- Benchmark federated models vs public/external resources (SAbDab, OAS, TAP, Jain panel, FLAb, emerging datasets) and quantify lift.
- Develop cross-domain validation (formats/modalities, expression systems, partner assay protocol differences) and temporal/concept-drift-aware validation.
- Partner on model design choices impacting validation (architecture, feature design, uncertainty quantification, calibration, endpoint combination).
- Ensure statistical rigor, reproducibility (MLOps/data/model/versioning), performance profiling, and platform integration with TuneLab (NVIDIA FLARE).
Qualifications (Required)
- PhD in Computational Biology/Bioinformatics/Computational Chemistry/CS/Statistics or related.
- 4+ years post-PhD antibody discovery/engineering/developability experience.
- Experience analyzing/modeling antibody developability assay data (e.g., HIC, AC-SINS, nanoDSF, polyspecificity panels, viscosity, chemical liabilities).
- Antibody numbering tools (ANARCI or equivalent) and Kabat/Chothia/IMGT knowledge.
- ML validation protocol design for biological sequence data (similarity-aware splits, held-out test design).
Preferences (Preferred)
- Protein/antibody language model fine-tuning (e.g., ESM-2, AbLang, IgBERT, AntiBERTa), liability motif knowledge, uncertainty quantification/calibration.
- NVIDIA FLARE/federated frameworks; structure prediction tools; PyTorch + ML ecosystem; MLflow/W&B; relevant publications; strong technical writing.
Location/Travel
- Indianapolis, San Francisco, or Boston; up to 10% travel.
Compensation & Benefits
- Anticipated wage: $166,500–$266,200; potential company bonus; eligible for 401(k), healthcare, life insurance, time off/leave, and well-being benefits.