Scientific Machine Learning Engineer

Our Hit Discovery team builds computational workflows that combine machine learning with chemical physics to efficiently evaluate billions of potential drug molecules. As a member of this group, you’ll collaborate with research scientists on improving the accuracy and applicability of these methods and turn them into robust, scalable software products.

• Design, build, and test scalable software for ML virtual screening pipelines, integrating ML applications with physics-based backends (such as docking and free energy perturbation) to optimize hit and hit-to-lead campaigns
• Work closely with scientists to adapt and expand our existing workflows for new discovery cases, as well as help develop new methodologies with our current pipelines
• Explore scientific literature and benchmark novel ML approaches to improve the applicability and accuracy of core hit discovery workflows