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