Hiring nowMedical imaging and diagnostic devices

Medical Imaging AI Engineer

Also called Radiology AI Engineer

A Medical Imaging AI Engineer builds image-analysis functions for a defined clinical use and shows they work on representative clinical data. They turn a promising model into something a clinician, a regulator and a customer can trust.

What they do

They build and test models that read medical images for one clearly stated clinical purpose. Much of the week goes on the data: checking where images came from, how they were acquired, how they were labelled by clinicians, and whether the sample matches real patients. They train models, then evaluate them on clinical data split by scanner, site and patient group. They study the errors with clinical colleagues to see which ones matter. They also work with quality and regulatory teams, keeping records that show how the model was built, tested and changed.

What a strong hire delivers

In the first months a strong hire gives the company a working imaging model for one defined use, and an evaluation that clinicians accept as representative. They set out sensitivity and specificity with the errors that carry clinical weight, broken down by subgroup. They leave the data pipeline and test records in a state that quality and regulatory colleagues can build on without reconstruction.

How you can tell they are good

Ask them to walk through one imaging model they took towards clinical use. Listen for how they chose and checked the data, who defined the ground truth, and what they did when performance differed between sites or groups. Good candidates name specific errors that would harm a patient. Be wary of anyone who quotes one headline accuracy figure and stops there.

Why it is hard to fill

The work sits between machine learning, clinical science and regulated device development, and few people have all three. Titles vary: radiology AI engineer, imaging scientist, computer vision engineer in health. Many strong ML engineers have never faced clinical validation or quality systems. Many clinical or imaging scientists have limited modelling depth. Founders often get a good half of the profile and miss the rest.

Where to find them

Look in medical imaging companies, diagnostic device makers, hospital research groups and academic imaging labs. Typical titles include imaging scientist, biomedical engineer, computer vision engineer, medical physicist with strong coding, and ML engineer in a regulated health team. Some come from radiology departments or clinical science. Others move across from general computer vision after working on health data.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Medical Imaging AI Engineer

  1. Tell us about an imaging model you took towards clinical use. What was the intended use and who defined the ground truth?

    A strong answer states a narrow clinical purpose, explains how labels were produced and how disagreement between readers was handled. It names limits of the data. Worry if the intended use is vague or labels are treated as unquestionable.

  2. How would you build an evaluation that is representative of the patients and devices the model will meet?

    Look for splits by site, scanner, protocol and patient group, no leakage between patients, and a sample that mirrors real use. They should discuss prevalence and missing groups. Worry about random image-level splits and a single test set.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building AI functions for a diagnostic imaging device and need an engineer to develop and validate image-analysis models for a defined clinical use. You will work closely with clinicians, quality and regulatory colleagues, and the work is judged on clinical evidence, not demos.

What you will do

  • Develop image-analysis models for a defined clinical use
  • Curate and check imaging data, including source, acquisition and labelling
  • Design clinical evaluations that represent real patients, sites and devices
  • Report sensitivity, specificity and clinically meaningful errors by subgroup
  • Quantify and communicate model uncertainty
  • Maintain records that support quality and regulatory review
  • Review errors with clinical colleagues and act on findings

What you will bring

  • Demonstrated medical-image analysis work taken beyond a prototype
  • Experience curating imaging data and handling label disagreement
  • Strong computer vision and model evaluation skills
  • Experience designing evaluations split by site, scanner or patient group
  • Ability to explain clinical consequences of model errors
  • Comfort working within a quality system and keeping traceable records

Nice to have

  • Experience with a regulated device submission
  • Background in clinical science or medical physics
  • Experience with uncertainty estimation in deployed models

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Computer visionAdvancedAnalyse or generate visual information
  • Clinical validationAdvancedAssess intended clinical benefits, harms and performance
  • AI governance and assuranceWorkingAssign accountability and verify lifecycle controls
  • Uncertainty quantificationWorkingEstimate confidence, calibration and model uncertainty

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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