Machine Learning Engineer

MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA. They will be developing and deploying advanced computational methods for molecular design. This is a 12-month hybrid contract. About the Role • Build pipelines for probabilistic molecular property prediction and Bayesian acquisition to power active learning–driven drug discovery. • Engineer workflows for molecular generative modeling and other innovative design approaches. • Collaborate with machine learning scientists, engineers, computational chemists, and biologists. • Partner with therapeutic development teams to analyze existing molecules and design new candidates. • Contribute to ongoing initiatives while driving new research directions. Qualifications • PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field — OR MS + 3+ years of relevant industry experience. • Demonstrated expertise in production-ready ML workflows (e.g., PyTorch + Lightning + Weights & Biases). • Strong track record of achievement (e.g., high-impact first-author publication or equivalent). • Excellent written, visual, and verbal communication skills. Preferred Experience • Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit). • Background 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, or statistical methods. • Hands-on experience with Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch. • Public portfolio of computational projects (e.g., GitHub).