EmergingMedical imaging and diagnostic devices

AI Device Post-Market Specialist

Also called AI Device Surveillance Specialist

An AI Device Post-Market Specialist watches how an AI-enabled diagnostic or monitoring device performs once it is in clinical use. They catch drift and emerging harms early, which protects patients, regulatory standing and customer trust.

What they do

They design and run the surveillance plan for a deployed AI device. They set which performance signals to track, such as subgroup accuracy, image quality shifts and override rates, and where the data comes from. They review field data and complaints, run periodic performance checks against reference sets, and investigate anything that looks off. They judge whether a change to the model, scanner mix or clinical workflow needs formal change control. They write performance and incident reports for quality, regulatory and clinical teams, and they agree actions with engineers when drift appears.

What a strong hire delivers

In the first months, a strong hire puts a written surveillance plan in place with clear metrics, thresholds and owners. They set up a regular reporting rhythm that quality, clinical and engineering teams actually read. They find any gaps in current field data collection and close the most important ones. They also give the company a working change control routine, so model updates are assessed and recorded consistently.

How you can tell they are good

Ask them to walk through a surveillance plan they built or critiqued. Good candidates explain why they chose each signal, how they set thresholds, and how they separated real drift from noise. Ask for an incident they handled from first signal to closure. Be wary of people who only talk about offline accuracy, or who treat monitoring as a dashboard rather than a decision process.

Why it is hard to fill

Almost nobody holds this title yet. The work usually sits inside quality, clinical affairs, ML engineering or regulatory roles, so CVs rarely say what the person has done. The mix is unusual: you need statistical and ML fluency, clinical sense and comfort with quality systems. Most candidates have two of the three. Hiring managers often search on the wrong title and miss the right people.

Where to find them

Look at clinical scientists, biomedical engineers and imaging physicists who moved into quality or regulatory work on software-based devices. Also look at ML engineers in medical imaging who owned validation or field performance. Good sources are imaging AI developers, scanner makers, digital health companies and hospital groups running their own algorithm assurance. Titles often read as quality, clinical validation or safety engineer.

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 an AI Device Post-Market Specialist

  1. Describe a surveillance plan you wrote or reviewed for an AI device. What did you choose to monitor, and why?

    A strong answer links each signal to a specific clinical risk and names data sources, thresholds and owners. It covers subgroup performance and workflow effects. Worry if the answer is generic, or limited to headline accuracy.

  2. How would you tell genuine model drift from a change in scanner mix or patient population?

    Good answers stratify by site, device and demographic, compare input distributions, and check against reference data before blaming the model. They accept uncertainty. Worry if they jump straight to retraining or cannot describe how to isolate causes.

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

Job description you can post

We build AI-enabled diagnostic and monitoring devices that clinicians rely on. We are hiring someone to watch how those devices perform once they are in use, spot drift and emerging harms early, and keep our change control sound. You will work across engineering, clinical and quality teams.

What you will do

  • Design and maintain the surveillance plan for our deployed AI devices
  • Define performance signals, thresholds and data sources for field monitoring
  • Review field data, complaints and incidents and lead investigations
  • Run periodic performance checks against reference datasets
  • Assess model and workflow changes through change control
  • Write clear performance and incident reports for quality, clinical and regulatory teams
  • Agree corrective actions with engineering and track them to closure

What you will bring

  • Demonstrated work monitoring or validating the performance of a deployed AI or software-based medical device
  • Working knowledge of computer vision models and how they fail
  • Experience with clinical validation or performance studies
  • Ability to apply uncertainty quantification and subgroup analysis to real data
  • Familiarity with quality systems and change control in a regulated setting
  • Clear writing that non-specialists can act on

Nice to have

  • Experience with medical imaging toolchains and device testing
  • Background in AI governance or assurance
  • Experience handling regulator-facing incident reporting

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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