Radiologist - Clinical Benchmarks

Experience


Skills

• MD/DNB Radiology
• Benchmark design
• Ground-truth labeling
• Adjudication protocols
• Inter-reader variability
• Model evaluation

Job Summary

• 5Cs AI is judged against what its radiologists say is correct. This role owns that standard. You will design the gold-standard benchmark datasets, adjudication protocols, and ground-truth labeling standards that Bionics models are measured against - turning clinical judgment into evaluation the Applied AI team can build to.
• You are an NMC-registered radiologist with rigorous reporting standards and an interest in how models fail. Any subspecialty is welcome; depth in MSK, neuroradiology, or chest imaging is particularly valued. You will not be working through a reading worklist - you will define what a correct read looks like, case by case, so model performance is measured against something real.
• The pitch is simple: your expertise, amplified. A single well-adjudicated benchmark case shapes the behaviour of models that read millions of scans. This is the radiologist who teaches machines what excellent looks like.
• We are strongly biased toward candidates who can join quickly. If your notice period is short, your application will move faster because these teams are being built now.

You Should Have

• MD or DNB in Radiology with active NMC registration
• Rigorous personal reporting standards and the ability to articulate why a read is correct, not just what the read is
• Comfort with structured criteria: you can turn "I know it when I see it" into a written standard another radiologist can apply consistently
• Interest in how AI models behave and fail. You do not need to write code, but you need to reason clearly about sensitivity, specificity, false positives, false negatives, and calibration
• The temperament for adjudication: patient with disagreement, precise in documentation, and comfortable being the final word on ground truth

Even Better If You Have

• Subspecialty depth in MSK, neuroradiology, or chest imaging
• Experience with reader studies, research methodology, or statistics: kappa, ROC analysis, cohort design
• Teaching or peer-review experience - you have already spent time explaining what separates an adequate read from an excellent one
• Prior exposure to AI model evaluation, annotation programs, or clinical quality assurance

Why 5C

• Your judgment scales. A benchmark you adjudicate once shapes model behaviour across every scan those models read
• You sit at the centre of the Applied AI team, not at its edge. Ground truth is the constraint everything else is built around
• 5C reads scans for 2,000+ hospitals and diagnostic centers, so the standards you set propagate through one of the largest radiology operations in the country

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