Staff Software Engineer / Machine Learning Engineer - Radiology

Machine Learning Engineer 

Key Responsibilities

• Develop, train, andvalidatestate-of-the-artML/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)

• Strongproficiencyin 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 tooperateeffectively in a translational, regulatory-aware environment

• Strong interest in delivering clinically impactful AI solutions