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

• An applied problem solver who’s excited about addressing challenging problems in computational chemistry and thrives when working with large codebases and data sets
• A growth-oriented engineer who’s eager to adopt best practices like automated testing and code reviews
• A collaborative communicator who enjoys bridging the gap between ML, software engineering, and scientific research

What you’ll do:

• 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

What you should have:

• 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

We’d love to hire someone with:

• 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