Eli Lilly

Seeking a scientist-engineer hybrid to deploy AI-driven discovery platforms using foundation models, multi-agent systems, and robotics in drug discovery workflows. The role involves translating scientific workflows into agentic systems, integrating LLM reasoning with domain tools, and supporting model deployment and inference services.

This role focuses on building AI-orchestrated pipelines for biotherapeutics discovery, connecting generative design, property prediction, experiment selection, and result interpretation into a semi-autonomous loop with human oversight. It involves designing active learning pipelines for multi-objective optimization and using reinforcement learning to align generative models for desired therapeutic properties.

Associate Vice President leading the analytical methods and computational science pillar within Data Foundry, focusing on accelerating molecule discovery. This role involves leading a team of experts in cheminformatics, computational structural biology, statistical modeling, and AI/ML, ensuring Lilly has access to advanced analytical approaches for both human scientists and AI agents. The position requires strategic decision-making on adopting or developing methods, identifying data gaps, and establishing validation frameworks, with a strong emphasis on integrating methods into AI agent workflows.

This role focuses on applying statistical and computational approaches, including machine learning, to analyze large-scale multi-omics (genomic, proteomic, metabolomic) and clinical data from biobanks and population cohorts. The goal is to identify novel therapeutic targets and biomarkers for cardiometabolic diseases. The role involves developing and implementing bioinformatics pipelines, contributing to novel statistical methods, and collaborating with interdisciplinary teams to guide therapeutic development. While the primary focus is on data analysis and method development (L0/L2), the ultimate aim is to inform drug discovery and development.

This role focuses on applying AI/ML and automation within Chemical, Manufacturing, and Controls (CMC) disciplines in pharmaceutical R&D. The scientist will derive insights from data, build decision support tools, leverage LLMs to enhance data science workflows, and contribute to developing autonomous laboratories through AI-driven methodologies and lab automation. The primary focus is on data preparation, feature engineering, and potentially building agentic systems for experimental design and execution.

Postdoctoral Scientist role focused on developing synthesis-aware virtual screening workflows for early small-molecule drug discovery, integrating AI/ML with structure-based drug design and fragment chemistry.

Computational Biologist role focused on analyzing multimodal biological datasets (spatial and single-cell omics, proteomics, metabolomics, functional genomics) to advance target discovery in cardiometabolic diseases. The role involves developing predictive models, integrating diverse data types, applying AI/ML and causal inference methods, and influencing data architecture and analytical standards. Collaboration with internal AI and data engineering teams is key.