Radiologist Expert

Experience: 3–5 years

Job Type: Full-time (Contractual / Freelancing)

Location: Remote

Required Skills: Clinical communication, Radiological expertise, Diagnostic accuracy

Join our team as a Radiologist - AI Trainer and play a pivotal role at the forefront of clinical innovation. As an expert in radiology, you will leverage your diagnostic acumen and communication skills to refine AI models that impact modern healthcare. This position offers the unique opportunity to work remotely, collaborating closely with world-class AI researchers and data experts.

Key Responsibilities

• Review and accurately annotate a wide range of radiological images (CT, MRI, X-ray) to support the training and validation of advanced AI models.
• Evaluate AI-generated reports and outputs, ensuring diagnostic accuracy, clinical clarity, and alignment with best practices.
• Provide detailed, expert feedback on model performance, identifying edge cases and highlighting clinical nuances to enhance AI learning.
• Collaborate seamlessly with AI researchers and multidisciplinary data teams, bridging clinical expertise with technological innovation.
• Ensure the consistent interpretation of medical images in accordance with current clinical guidelines and ethical standards.
• Uphold high standards of data quality, patient privacy, and clinical relevance in all annotations and reviews.
• Communicate findings and insights both verbally and in writing, supporting the iterative improvement of AI solutions.
  
  

• Board-certified Radiologist or equivalent international qualification.
• Minimum 3–5 years of hands-on clinical experience in interpreting medical imaging.
• Proficiency with PACS and radiological reporting tools.
• Demonstrated clinical communication skills, with a strong emphasis on both written and verbal feedback.
• Exceptional attention to detail, image quality, and pathology assessment.
• High level of English proficiency; comfortable critiquing and documenting findings in writing.
  
  

• Prior experience with AI/ML initiatives or medical data annotation platforms.
• Exposure to interdisciplinary teams focused on clinical informatics or digital health innovation.