Staff Software Engineer / Machine Learning Engineer - Radiology

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. This role focuses on building robust algorithms for segmentation, quantification, and detection across CT, MRI, and X-ray. This position sits at the center of a well-resourced, data-rich research environment with established infrastructure for multi-institutional data aggregation, curation, and large-scale annotation. St. Jude Children’s Hospital has incredible high-performance computing resources. The lab leverages curated datasets from diverse sources, dedicated annotation teams, and external engineering support, enabling this role to focus on high-impact model development, validation, and clinical translation. Many projects are designed with a path toward regulatory clearance via FDA’s 510(k) or De Novo pathways, and the successful candidate will work closely with regulatory and quality experts to support reproducible, well-documented, and clinically deployable AI solutions. This role offers a unique combination of academic productivity (authorship opportunities) and real-world impact through translation into clinical practice. Machine Learning Engineer    Key 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 tools (e.g., LLM-based coding agents) to accelerate development and improve code quality   Participate in team-based development practices (code reviews, Git, testing frameworks)   Support manuscripts, grants, and technical reporting   Minimum Education and/or Training: Bachelor's degree in computer science, data science, information science, business, or related field. Master's degree preferred. Minimum Experience: Minimum Requirement: Bachelor's degree with 5+ years of experience required. Experience Exception: Master's degree with 3+ years of experience. Experience with programming languages, databases, and software development lifecycle Experience with the position-specific technical stack preferred Experience with the position-specific scientific domain preferred Proven performance in earlier role/comparable role Preferred Qualifications   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)   Experience collaborating with regulatory, quality, or cybersecurity teams   Exposure to software security principles (e.g., secure coding, vulnerability assessment, penetration testing concepts)   Experience with large, multi-institutional datasets   Familiarity with radiology workflows and quantitative imaging biomarkers   Experience with cloud or high-performance computing environments   Experience deploying models into research or clinical environments   ​​​​​ Academic and Career Development Opportunities   Significant opportunities for authorship on high-impact manuscripts   Active participation in multi-institutional research collaborations   Opportunities to contribute to grant proposals and funded research initiatives   Exposure to translational AI development, including projects targeting FDA 510(k) clearance   Ability to build a strong academic portfolio in parallel with real-world clinical impact   Key Attributes   Highly collaborative and team-oriented   Detail-oriented with strong commitment to documentation, reproducibility, and auditability   Able to   operate   effectively in a translational, regulatory-aware environment   Strong interest in delivering clinically impactful AI solutions   Compensation In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the compensation range for this role. This is an estimate offered in good faith and a specifi