Radiologue (MD), Annotation et évaluation d'IA en imagerie médicale

About the Role

Mercor is hiring board-certified Radiologists for non-clinical work developing and evaluating clinical AI systems in medical imaging. You will apply your diagnostic expertise to annotation, reference-report authoring, and evaluation work that sets the standard these systems are measured against.

This is a non-clinical role, no direct patient care, and no responsibility for live diagnosis.

This is a shared expert pool. After onboarding you may be matched to any of several concurrent imaging workstreams based on your subspecialty, availability, and interest. You are not committing to a single project, and you may move between streams as priorities shift.

What you may work on

Work varies by workstream and may include:

• Case interpretation with written reasoning, working through imaging-based clinical cases that pair studies with patient context, recording your diagnostic reasoning step by step rather than only the final impression.

• Structured finding labelling, reading studies and labelling discrete findings against a defined schema: presence, location, laterality, severity, and interval change.

• Reference report authoring, writing the report a study should produce, to serve as ground truth for evaluation.

• Output review, judging AI-generated reads against clinical standards, flagging hallucinated findings, missed findings, and unsupported certainty.

• Grading criteria and guideline authoring, defining what separates an excellent read from a merely acceptable one, so judgment can be applied consistently at scale.

• Difficult-case writing, constructing imaging questions that probe the limits of current model reasoning.

Task length varies by stream, from roughly 10 minutes for structured labelling up to an hour for full case authoring. You will get a specific throughput target for whichever stream you are matched to.

Required qualifications

• MD or DO with completed diagnostic radiology residency

• Board certification, ABR, or the equivalent certifying body in your jurisdiction, or board-eligible with residency complete

• Active, unrestricted medical license in your country of practice

• 2+ years post-residency reading independently

• Currently or recently practicing, with a record of high-volume independent reads

• Precise command of structured reporting standards (BI-RADS, LI-RADS, Lung-RADS, TI-RADS and similar, as relevant to your subspecialty)

• Written and spoken English fluency

• Minimum 15 hours per week, with the ability to concentrate hours when a stream is time-boxed

Preferred qualifications

• Fellowship subspecialty training, neuroradiology, musculoskeletal, body/abdominal, chest, breast, or pediatric imaging

• U.S. licensure and board certification

• Prior medical image annotation, AI evaluation, or dataset curation experience

• Teaching, rubric design, or resident assessment experience

• Published research or sustained technical writing (please link a sample)

Why this work

Imaging is where medical AI is closest to real clinical deployment, and where a confidently wrong answer costs the most. The reference standard these models get graded against is written by radiologists, here you would be writing it.