Exceptional candidates with an MSc and equivalent depth of experience will be considered; Deep, current expertise across modern ML for molecules: graph neural networks, transformers, generative approaches (diffusion, flow matching, autoregressive), Bayesian optimization and active learning, transfer learning on sparse assay data, and uncertainty quantification; Working command of the discovery domain: SAR interpretation, multi-parameter optimization, ADMET and developability, protein structure and ligand binding, docking and free energy methods; Demonstrated impact on real programs - molecules advanced, cycles shortened, decisions changed. Experience with modalities beyond conventional small molecules - PROTACs and molecular glues, macrocycles, peptides, covalent inhibitors, or oligonucleotides; Familiarity with lab automation, self-driving lab concepts, or high-throughput chemistry integration; Experience evaluating or deploying large-scale pretrained models for chemistry or protein structure; Prior experience in a large matrixed pharma R&D organization, or scaling a capability from startup to enterprise; Strong external profile: publications, conference presence, or leadership in precompetitive consortia.