## The Role
* 5C's 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 Bionic's 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.
## What You Will Own
* Gold-standard benchmark datasets: case selection, cohort design, difficulty stratification, and subspecialty coverage across CT, MRI, X-ray, and mammography
* Adjudication protocols: consensus panels, tie-break rules, escalation paths, and documentation that makes every ground-truth decision auditable
* Ground-truth labeling standards: finding definitions, severity scales, laterality and measurement conventions, and rubrics precise enough that a second radiologist applying them reaches the same answer
* Inter-reader variability: measuring it, deciding when disagreement is signal versus noise, and designing consensus processes that converge without flattening legitimate clinical judgment
* Model failure review: working daily with the Applied AI team to examine where models diverge from ground truth, and deciding whether the model, the label, or the benchmark is wrong
* Evaluation criteria: translating clinical judgment into measurable definitions of correctness — what counts as a miss, what counts as a clinically irrelevant difference, what counts as false reassurance
## 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
* 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.
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