Hybrid Radiology IT Systems Specialist

Washington University in St. Louis; Missouri; Hybrid; USD 53,000 - 91,000; Full time

Benefits offered by this job

Health insurance

Vacation days

Transit benefits

Job summary

Washington University in Saint Louis, MO, invites applications for an Operations & System Specialist I (Hybrid) supporting Radiology. The role focuses on enabling researchers and students to run AI models on the CHPC, manage Jupyter notebooks, Python environments, and DL workflows within a medical imaging context.

The position emphasizes Linux-based HPC administration, DICOM processing, and user training to ensure robust, secure, and efficient workflows for AI-enabled radiology studies.

Qualifications

• Bachelor’s degree or combination of education and/or experience may substitute for minimum education.
• 3 years of related work experience in Operations Analysis, Design, or a closely related field.
• Experience with Linux, HPC, AI frameworks (PyTorch/TensorFlow) and data workflows.

Responsibilities

• Communicate features to prospective and existing users and provide in-person training sessions.
• Assist students and researchers in configuring and running AI models on HPC resources.
• Support Jupyter notebooks, Python environments and debugging on CHPC infrastructure.
• Guide usage of PyTorch, TensorFlow and DL techniques in medical imaging contexts.
• Collaborate on DICOM image processing and segmentation tasks as needed.

Skills

PyTorch

TensorFlow

Python

Jupyter

Linux

DICOM

C

C++

Deep Learning

Shell Scripting

Education

Bachelor's degree

Tools

Jupyter Notebook

Linux

Job description

Washington University in Saint Louis, MO, invites applications for an Operations & System Specialist I (Hybrid) supporting Radiology. The role focuses on enabling researchers and students to run AI models on the CHPC, manage Jupyter notebooks, Python environments, and DL workflows within a medical imaging context.

The position emphasizes Linux-based HPC administration, DICOM processing, and user training to ensure robust, secure, and efficient workflows for AI-enabled radiology studies.