Staff Software Engineer / Machine Learning Engineer - Radiology

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
• 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 Qualifications

• Bachelor's degree in computer science, data science, information science, business, or related field
• Minimum 5+ years of experience required

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
• 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