Image Analysis & Microscopy RSE for AI-Driven Discovery

Johns Hopkins University; Baltimore (MD); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Johns Hopkins University’s Data Science and AI Institute seeks a Research Software Engineer specializing in image analysis and microscopy. You will design and build state-of-the-art AI software for diverse scientific domains, collaborating with affiliated faculty on short-term engagements and long-term initiatives.

The role emphasizes professional software engineering practices, data-intensive workflows, and potential co-authorship on publications, with a hybrid work arrangement at Mount

Qualifications

• Masters in a quantitative discipline with strong scientific computing background.
• Three years of software development experience in large projects.
• Experience in AI/ML modeling or data science pipelines at scale.
• Willingness to lead, teach, and collaborate with faculty and engineers.

Responsibilities

• Collaborate in a team with RSEs and scientists on advanced software solutions.
• Contribute to AI/ML solutions using modern libraries and hardware.
• Analyze large data sets in cloud or on-premises environments.
• Design data models and software pipelines for real-time data processing.
• Co-author publications and translate project themes into reusable frameworks.

Skills

Python

C++

AI/ML libraries

Docker

RESTful services

SQL

Linux

Git

CI/CD

Cloud development

Leadership

Teaching / mentoring

Education

Master's degree in quantitative discipline

PhD (preferred)

Three years software development experience

Tools

Docker

Singularity

Git

CI/CD tooling

Job description

Johns Hopkins University’s Data Science and AI Institute seeks a Research Software Engineer specializing in image analysis and microscopy. You will design and build state-of-the-art AI software for diverse scientific domains, collaborating with affiliated faculty on short-term engagements and long-term initiatives.

The role emphasizes professional software engineering practices, data-intensive workflows, and potential co-authorship on publications, with a hybrid work arrangement at Mount