Clinical Imaging & Machine Learning (ML) Validation Specialist

Job Title: Clinical Imaging & Machine Learning (ML) Validation Specialist
Location: Remote
Duration: Long Term
Experience: 8 Years

Job Summary

The Clinical Imaging & ML Validation Specialist is responsible for validating, evaluating, and clinically contextualizing machine learning models applied to medical imaging. This role bridges clinical imaging expertise (radiology, pathology, cardiology, etc.) with data science and regulatory rigor to ensure ML models are accurate, reliable, unbiased, and clinically safe for real-world deployment.

The specialist works closely with data scientists, engineers, clinicians, regulatory teams, and quality assurance to design and execute validation studies, curate and annotate imaging datasets, assess model performance, and support regulatory submissions and post-market monitoring.

Key Responsibilities

Clinical & Imaging Expertise

• Apply deep understanding of clinical imaging workflows (e.g., radiology, pathology, ultrasound, CT, MRI, X-ray) to guide ML validation efforts
• Interpret imaging data and clinical context to ensure model outputs are clinically meaningful and safe
• Define clinical use cases, intended use, and relevant performance metrics for ML models
• Collaborate with clinicians to review model predictions and failure cases

ML Model Validation & Evaluation

• Design and execute validation protocols for imaging-based ML/AI models
• Evaluate model performance using clinically relevant metrics (e.g., sensitivity, specificity, AUC, PPV/NPV, calibration)
• Perform subgroup and bias analyses (e.g., by age, sex, device, site, pathology prevalence)
• Assess robustness, generalizability, and edge-case behavior of models
• Conduct reader studies and human-AI comparison studies when required

Data Curation & Annotation

• Oversee curation, quality control, and annotation of medical imaging datasets
• Define annotation guidelines and ensure inter-reader reliability
• Work with labeling teams, radiologists, and clinicians to resolve discrepancies
• Ensure datasets meet clinical, statistical, and regulatory standards

Regulatory & Quality Support

• Support regulatory submissions (e.g., FDA, CE, UKCA) with validation evidence and documentation
• Contribute to clinical evaluation reports, performance evaluation plans, and risk management files
• Ensure compliance with applicable standards (e.g., ISO 13485, ISO 14971, IEC 62304, Good Machine Learning Practice)
• Participate in audits, design reviews, and post-market surveillance activities

Cross-Functional Collaboration

• Act as a liaison between clinical teams, ML engineers, product managers, and regulatory affairs
• Translate clinical requirements into technical validation criteria
• Communicate validation results clearly to both technical and non-technical stakeholders
• Provide feedback to model development teams for iterative improvement

Post-Deployment Monitoring

• Monitor real-world performance of deployed ML models
• Investigate performance drift, data shift, and emerging clinical risks
• Support continuous learning and model update strategies

Required Qualifications

Education

• Advanced degree (Master’s or PhD preferred) in:
• Biomedical Engineering
• Medical Physics
• Computer Science (with medical imaging focus)
• Data Science
• Radiology / Clinical Sciences
• Or a related field

Experience

• 3–7+ years of experience in medical imaging, ML/AI validation, or clinical data analysis
• Hands-on experience with medical imaging modalities (CT, MRI, X-ray, ultrasound, pathology, etc.)
• Experience validating ML or AI models in healthcare or regulated environments
• Familiarity with clinical study design and statistical evaluation

Technical Skills

• Strong understanding of ML concepts and performance evaluation (no need to be a core model developer, but must be fluent)
• Experience with Python, R, or similar tools for data analysis and validation
• Familiarity with DICOM, PACS, and medical imaging data formats
• Knowledge of dataset bias, generalization, and model interpretability
• Experience with version control, experiment tracking, and documentation

Preferred Qualifications

• Clinical background (e.g., radiologist, pathologist, imaging scientist)
• Experience with FDA, CE, or other regulatory submissions for AI/ML medical devices
• Experience conducting reader studies or clinical performance studies
• Knowledge of health data privacy regulations (HIPAA, GDPR)
• Familiarity with post-market surveillance and real-world evidence collection

Key Competencies

• Strong analytical and critical-thinking skills
• Ability to balance clinical relevance with technical rigor
• Excellent written and verbal communication skills
• High attention to detail and documentation quality
• Ability to work effectively in cross-functional, multidisciplinary teams

Typical Work Outputs

• ML validation plans and reports
• Clinical performance metrics and analyses
• Bias and subgroup analysis documentation
• Regulatory-ready validation evidence
• Clinical feedback for model improvement