Machine Learning Engineer

Job Title: Machine Learning Engineer Location: 1 DNA Way, South San Francisco, CA 94080 Duration: 12-month contract (possibility of extension) Work Model: Hybrid About the Role Client’s Prescient Design team within the Research and Early Development (gRED) organization is seeking a Machine Learning Engineer to help advance structural and machine learning-based approaches for molecular design. You will play a key role in developing and deploying advanced ML techniques for molecular optimization, property prediction, and active learning-driven drug discovery. This is an exciting opportunity to contribute to cutting-edge science while collaborating with top-tier researchers in computational biology, chemistry, and drug development. Key Responsibilities • Develop and deploy machine learning and Bayesian optimization workflows for molecular property prediction and optimization. • Collaborate with scientists across Prescient Design and gRED to design, analyze, and optimize small and large molecule therapeutics. • Engineer production-ready pipelines for probabilistic modeling, active learning, and molecular generative modeling. • Support drug discovery initiatives by applying ML models to enable target-driven design campaigns. • Contribute to existing research projects and help define new opportunities for machine learning in molecular science. Qualifications • PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or a related quantitative field • – or – MS degree with 3+ years of relevant industry experience. • Strong experience in software engineering and production-grade ML workflows using libraries such as PyTorch, Lightning, and Weights & Biases. • Proven research track record (e.g., at least one high-impact first-author publication or equivalent). • Excellent written, visual, and verbal communication skills with a collaborative mindset. Must Have Skills & Experience Candidates with strong expertise in one or more of the following areas are highly encouraged to apply (listed in order of importance): • Molecular property prediction • Probabilistic modeling and inference • Bayesian optimization or active learning • Production software engineering or pipeline optimization • Cheminformatics Additional desirable skills include: • Experience with physical modeling methods (e.g., molecular dynamics). • Familiarity with cheminformatics toolkits (e.g., RDKit). Background in: • De novo drug design • Computational or medicinal chemistry • Small molecule design • Self-supervised or geometric deep learning • Statistical modeling and data analysis • Public portfolio (e.g., GitHub) demonstrating computational or ML projects.