Posting Description
Chemical Engineering (ChemE)-Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) Consortium, to be partially responsible for the continued development and maintenance of command-line and web-based applications deploying machine learning (ML) models for chemical synthesis planning, property prediction, and molecular design and will work closely with faculty, researchers, and graduate students to translate scientific and engineering workflows into robust, scalable, and reproducible computational applications. Responsibilities include professionalization of software developed by graduate students/postdocs/MLPDS team members; maintaining and continuing the development of a modern web application for submitting and processing long-running ML tasks and storing, retrieving, visualizing, and analyzing results; developing API standards and data structures for ML predictions for synthesis planning, property prediction, and molecular design; defining and codifying reproducible and transferable ELT pipelines for training ML models on chemical data; preparing scripts for facilitating the automatic retraining and deployment of ML models on user-provided chemical and reaction datasets; packaging the application into containerized microservices for deployment using Docker and Kubernetes/EKS; and monitoring application usage and setting resource limits.
Job Requirements
REQUIRED: Bachelor’s degree in computer science, Chemical engineering, Chemistry, or a closely related discipline; a minimum of three years of experience with software/web development; strong preference for experience working with interdisciplinary teams on Python applications in the physical sciences; and familiarity with Tensorflow, PyTorch, or related ML code and version control workflows (e.g., Git and GitHub/GitLab); experience working with web-based, single-page applications using frameworks such as Vue or React; and experience supporting applications in a containerized (docker) and cloud-based infrastructure (AWS/EKS). PREFERRED: Familiarity with cheminformatics tools, e.g., RDKit; familiarity with molecular machine learning, chemical synthesis planning, and/or property prediction; and experience working in interdisciplinary academic/research environments.
9/1/2026