Software Developer

IMDiversity; Cambridge (MA); Hybrid; USD 79,000 - 106,000; Full time

Job summary

IMDiversity in Cambridge, MA seeks a Software Developer to advance machine learning-enabled chemical engineering applications and services. You will build and maintain command-line and web tools that deploy ML models for synthesis planning and property prediction, collaborating with faculty, researchers, and students.

You will containerize services with Docker and Kubernetes, develop scalable ELT pipelines, and help define APIs for ML deployment on chemical data.

Qualifications

• Bachelor's degree in computer science, chemical engineering, chemistry, or closely related field.
• Minimum of three years of experience with software/web development.
• Experience with Python applications in physical sciences.
• Familiarity with TensorFlow, PyTorch, or related ML code and version control workflows (Git, GitHub/GitLab).
• Experience with web-based, single-page apps using Vue or React.
• Experience in containerized and cloud-based infrastructure (Docker, AWS/EKS).

Responsibilities

• Professionalize software developed by team members.
• Maintain and develop a modern web application for long-running ML tasks and result visualization.
• Develop API standards and data structures for ML predictions in synthesis planning and property prediction.
• Define reproducible ELT pipelines for training ML models on chemical data.
• Prepare scripts to retrain and deploy ML models on user datasets.
• Package the application into containerized microservices for deployment using Docker and Kubernetes/EKS.
• Monitor application usage and set resource limits.

Skills

Python

ML frameworks

Web development

Git/GitHub

Docker

Kubernetes

Vue/React

Education

Bachelor's degree in CS, Chemical Eng, or Chemistry

Tools

Docker

Kubernetes/EKS

Git

Vue

React

TensorFlow/PyTorch

Job description

Software Developer

Job Number: 25997

Functional Area: Information Technology

Department: Chemical Engineering

School Area: Engineering

Pay Range Minimum: $79,050

Pay Range Maximum: $106,420

Employment Type: Full-time (Hybrid)

Employment Category: Exempt

Visa Sponsorship Available: No

Schedule:

Pay Grade: 8

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; packagingthe 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.