Machine Learning Engineer

Machine Learning Engineer Location: 1 DNA Way, South San Francisco, CA 94080 Duration: 1-Year Contract (with possibility for extension or conversion) Overview An innovative research and development team is seeking a Machine Learning Engineer to develop structural and machine learning-based methods for molecular design. The position focuses on molecular optimization for both small and large molecule drugs. Key focus areas include: • Probabilistic molecular property prediction • Bayesian acquisition for active learning-based drug discovery • Molecular generative modeling (potential involvement) Key Responsibilities • Collaborate with computational scientists, engineers, chemists, and biologists across multidisciplinary teams. • Develop machine learning and Bayesian optimization workflows to analyze existing and design new molecular structures. • Engineer pipelines for probabilistic property prediction, Bayesian acquisition, and generative modeling. • Partner closely with small molecule and protein therapeutic development teams. • Contribute to ongoing projects and propose innovative new initiatives. Required Qualifications • PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics or a related quantitative field • — OR — MS degree with 3+ years of industry experience. • Proficiency with production-ready ML workflows (e.g., PyTorch, PyTorch Lightning, Weights & Biases). • Track record with at least one high-impact first-author publication or equivalent achievement. • Strong written, visual, and oral communication skills. Desired Qualifications • Experience with molecular dynamics, physical modeling, and cheminformatics toolkits like rdkit. • Expertise in molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, and statistical methods. • Public computational project portfolio (e.g., GitHub).