RapidAI is the trusted leader in deep clinical AI, helping hospitals deliver faster, more informed care through intelligent imaging and integrated workflows. The Rapid Enterprise™ Platform supports disease states across the care spectrum, but it’s our clinical depth that drives the most meaningful impact — improving decision-making, patient outcomes, and health-system performance. Used by more than 2,500 hospitals in over 100 countries and backed by 700+ clinical studies, including research that helped expand national stroke-treatment guidelines, RapidAI is the most clinically validated AI platform in healthcare.
Design, develop, and optimize ETL pipelines for large-scale medical imaging datasets, ensuring efficient data ingestion, transformation, and storage.
Build and maintain medical imaging data repositories, ensuring seamless access,
query optimization, and compliance with healthcare regulations.
Implement data processing workflows for clinical imaging data (CT, MRI) to extract,
standardize, and structure metadata.
Develop scalable solutions for medical imaging data engineering, integrating with
PACS or XNAT, and other imaging systems.
Apply computer vision and machine learning techniques to analyze and process
medical images for healthcare applications, linking imaging data with associated
clinical metadata.
Extract and standardize image metadata (DICOM headers, HL7, FHIR) for enhanced
image classification and retrieval.
Develop automated image labeling and segmentation workflows using metadata
driven insights.
Optimize data storage and retrieval on AWS (S3, Lambda, EC2, DynamoDB, Redshift) for
high-performance clinical applications.
What you will bring:
7 to 10 years of experience in data engineering with a focus on healthcare imaging and
medical imaging data.
Expertise in medical imaging standards (preferably DICOM, but familiarity with other
formats is valuable) and clinical metadata processing.
