Responsibilities:

• Develop ML/AI models for discovery workflows (target prioritization, multi-omics integration, mechanistic inference).
• Apply modern ML (deep learning, graph learning, foundation/generative models) to chemical, biological, imaging, and assay data.
• Build/optimize scalable, interpretable, scientifically rigorous models for R&D use cases.
• Design, build, and maintain data pipelines to curate/standardize/integrate diverse R&D datasets (chemical, biological, multi-omics, imaging, biophysical, automation logs).
• Partner on MLOps/DevOps and deploy ML models into production R&D environments.
• Create tooling for dataset prep, feature engineering, and model lifecycle management.
• Collaborate with TD scientists to translate heterogeneous experimental data into insights for hit discovery, mechanism studies, perturbation experiments, and compound ...