Cheminformatics and Machine Learning Engineer Description Cheminformatics and Machine Learning Engineer (SE5 or Contractor) We are seeking a highly motivated Machine Learning Scientist to join Prescient Design within Genentech Research and Early Development (gRED) to help drive research on Machine Learning for Drug Discovery. The successful candidate will collaborate extensively with computational and experimental scientists and researchers across gRED to deploy and deliver machine learning solutions for small molecule drug discovery. The Role Implement cheminformatics and computational chemistry-based methods to support our Lab-in-the-Loop efforts for small molecule drug discovery. Deploy and deliver technical solutions at the intersection of computational chemistry, software engineering, and machine learning, supporting small molecule design across broader gRED and Roche. Closely collaborate with other scientists and researchers within Prescient to build impactful technologies for drug discovery research. Build and scale machine learning techniques to massive datasets and aid in the deployment of novel machine learning algorithms with experimental collaborators. Contribute to and drive publications, present results at internal and external scientific conferences, and help make code and workflows open source. Desired Qualifications BS, MS, or PhD degree in the physical sciences (e.g. Chemistry, Physics, Chemical Engineering) or quantitative field ( e.g. Computer Science, Statistics, Applied Mathematics) or equivalent industry research experience (5+ years for BS, 3+ years for MS). Excellent communication and interpersonal skills. Highly-motivated and independent self starter that is eager to collaborate. Expert in Python and experience with scientific software development for chemical modeling. Experience with RDKit or OpenEye Toolkits. Basic understanding of modern machine learning methods including predictive models, generative models, and active learning as applied to small molecule drug discovery. Additional Qualifications Candidates may additionally have, but are not required to have: Public portfolio of projects available on GitHub Extensive experience working with large chemical and biological datasets, including graph, sequence, and structure-based data Demonstrated experience with modern Python frameworks for deep learning like PyTorch Record of scientific excellence as evidenced by at least one first author publication in a scientific journal or machine learning conference Record of machine learning research excellence as evidenced by publications in computer science and machine learning conferences (e.g. NeurIPS, ICLR, ICML).