Staff Software Engineer / Machine Learning Engineer - Radiology

Job summary

Location Memphis, TN Category Computational Sciences Department Radiology Shift Weekday Day Position Type Full Time Scheduled Weekly Hours 40 Req # JR7176 JOB DESCRIPTION We are seeking a highly motivated and experienced Machine Learning Engineer to develop advanced machine learning (ML), deep learning (DL), and foundational AI models for medical imaging.

Benefits

Qualifications

• We are seeking a highly motivated and experienced Machine Learning Engineer to develop advanced machine learning (ML), deep learning (DL), and foundational AI models for medical imaging.
• 3+ years of experience developing ML/DL models for image analysis
• Demonstrated experience with segmentation, detection, and/or quantitative imaging algorithms (2D and/or 3D)
• Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
• Experience with modern architectures (U-Net variants, detection frameworks, transformers, or foundational models)
• Familiarity with DICOM and medical imaging workflows
• Strong understanding of evaluation metrics (Dice, IoU, ROC/AUC, sensitivity/specificity)
• Experience with version control and collaborative development (e.g., Git)
• Demonstrated ability to produce clear, structured technical documentation
• Experience using modern LLM-based coding assistants (e.g., Claude, Codex, or similar) to enhance development workflows
• Strongly preferred) Experience developing and documenting AI solutions for clinical translation or regulatory submission (e.g., FDA 510(k))
• Familiarity with Good Machine Learning Practice (GMLP)

Responsibilities

• Develop, train, and validate state-of-the-art ML/DL models for segmentation, quantification, and detection across CT, MRI, and X-ray
• Design and implement 2D and 3D model architectures (CNNs, transformer-based, and foundational models)
• Build scalable pipelines for data preprocessing, model training, evaluation, and deployment
• Develop quantitative imaging methods (e.g., volumetrics, density measurements, biomarker extraction)
• Leverage curated, multi-institutional datasets to ensure model generalizability and robustness
• Collaborate with radiologists and engineering teams to define clinically meaningful outputs
• Produce regulatory-grade documentation for datasets, model development, validation, and performance
• Ensure reproducibility and traceability of experiments (data, model, and code versioning)
• Work collaboratively with regulatory and quality experts to support FDA 510(k) and De Novo submissions, including providing technical documentation and validation evidence
• Contribute to software quality and security practices, including supporting activities such as vulnerability assessment and penetration testing in collaboration with cybersecurity and regulatory teams
• Utilize modern AI-assisted development

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