Staff Software Engineer / Machine Learning Engineer (Radiology)

Location: Memphis, Tennessee, United States

Salary: $104,000–$186,160 per year

Company

hirify.global operates a well-resourced, data-rich research environment focused on translating medical imaging research into clinical practice.

What you will do

• Develop, train, and validate ML/DL models for segmentation, quantification, and detection across CT, MRI, and X-ray.
• Design 2D and 3D architectures, including CNNs, transformer-based models, and foundational models.
• Build scalable pipelines for data preprocessing, training, evaluation, and deployment using multi-institutional datasets.
• Develop quantitative imaging methods such as volumetrics, density measurements, and biomarker extraction.
• Collaborate with radiologists, engineering teams, and regulatory and quality experts to define clinically meaningful outputs and support clinical translation.
• Produce technical documentation, validation evidence, manuscripts, grants, and reports while supporting software quality and security activities.

Requirements

• Bachelor's degree in computer science, data science, information science, business, or a related field, with 5+ years of experience; alternatively, a master's degree with 3+ years of experience.
• Experience with programming languages, databases, software development lifecycle, and collaborative development practices.
• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with ML/DL image analysis, segmentation, detection, quantitative imaging, or related medical imaging workflows.
• Ability to create clear technical documentation and maintain reproducible, traceable datasets, models, experiments, and code.
• Role based in Memphis, Tennessee, United States.

Nice to have

• Experience with U-Net variants, detection frameworks, transformers, foundational models, DICOM, and imaging biomarkers.
• Experience developing AI solutions for clinical translation or FDA 510(k) submissions and familiarity with Good Machine Learning Practice.
• Experience with cybersecurity, vulnerability assessment, penetration testing concepts, cloud or high-performance computing, and clinical deployment.
• Experience using LLM-based coding assistants and working with large multi-institutional datasets.

Culture & Benefits

• Opportunities for authorship on high-impact manuscripts and participation in multi-institutional research collaborations.
• Opportunities to contribute to grant proposals and funded research initiatives.
• Exposure to translational AI projects targeting FDA 510(k) clearance and real-world clinical impact.
• Access to high-performance computing resources, curated datasets, dedicated annotation teams, and external engineering support.