Scientific Machine Learning Engineer

Schrödinger•New York, NY

4d•$130,000 - $160,000•Hybrid

About The Position

We’re seeking a Scientific Machine Learning (ML) Engineer to join us in our mission to transform the discovery of therapeutics and materials! 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.

Requirements

Bachelor’s degree or higher in Chemistry, Physics, Computer Science, or a related field
Between one and three years of production-level software development experience
Prior ML knowledge (i.e., Reinforcement Learning or Active Learning) for predicting measurable properties of biological and/or chemical systems
Python programming proficiency and experience with ML frameworks like PyTorch

Nice To Haves

Familiarity with one of the following areas: molecular dynamics simulation, free energy calculation methods, ligand-based drug design, cheminformatics, quantum mechanics, virtual screening, and/or structured modeling
Experience with development of relevant commercial and/or open-source computational chemistry software

Responsibilities

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