Scientific Machine Learning Engineer

Sourced from aihiringboard.com

Schrödinger is seeking a Scientific Machine Learning Engineer in New York to join its Hit Discovery team, which builds computational workflows combining machine learning with chemical physics to evaluate billions of potential drug molecules. You will design and build scalable ML virtual screening pipelines, integrate ML applications with physics-based backends such as docking and free energy perturbation, and collaborate with research scientists to improve the accuracy and applicability of these methods. The role bridges ML, software engineering, and computational chemistry, turning research methods into robust, scalable software products.

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

• Bachelor's degree or higher in Chemistry, Physics, Computer Science, or a related field
• 1-3 years of production-level software development experience
• Prior ML knowledge (e.g., Reinforcement Learning or Active Learning) for predicting measurable properties of biological and/or chemical systems