Applied ML Scientist (Staff / Principal)

Role: Computational scientist specializing in AI-driven drug discovery, bridging research and experimental programs at Genesis Molecular AI. Responsibilities: assess and improve AI models for small molecule drug discovery, validate model predictions against project data, collaborate with ML, cheminformatics, and experimental teams, and contribute to dataset curation and experimental design. Requirements: PhD or equivalent in Cheminformatics, Computational Chemistry, or related; proven experience with machine learning in small molecule discovery; expertise in cheminformatics tools (RDKit/OpenEye), assay types, CADD workflows, and Python (scikit-learn, PyTorch); strong data analysis and validation skills. Preferred: advanced modeling techniques (graph neural networks, Bayesian optimization), large-scale data management, publications in ML for drug discovery. High-Value: focus on AI foundation models for molecular design, generative diffusion models, integrating physics and chemistry, supporting active drug programs. Work setup: likely hybrid with offices in San Mateo and San Diego; specific travel not stated.