Software Developer

MIT • Cambridge, MA

7d

About The Position

The Chemical Engineering (ChemE)- Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) Consortium is seeking a Software Developer 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. This role involves close collaboration with faculty, researchers, and graduate students to translate scientific and engineering workflows into robust, scalable, and reproducible computational applications. The position will focus on professionalizing software developed by the team, maintaining and enhancing a web application for ML tasks, developing API standards, defining ELT pipelines, preparing scripts for model retraining, packaging applications into containerized microservices, and monitoring application usage.

Requirements

• 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
• Familiarity with Tensorflow, PyTorch, or related ML code
• Familiarity with version control workflows (e.g., Git and GitHub/GitLab)
• Experience working with web-based, single-page applications using frameworks such as Vue or React
• Experience supporting applications in a containerized (docker) and cloud-based infrastructure (AWS/EKS)

Nice To Haves

• Familiarity with cheminformatics tools, e.g., RDKit
• Familiarity with molecular machine learning, chemical synthesis planning, and/or property prediction
• Experience working in interdisciplinary academic/research environments

Responsibilities

• 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
• Monitoring application usage and setting resource limits

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level