Hiring nowMedical imaging and diagnostic devices

AI Medical Device Validation Engineer

Also called AI SaMD Validation Engineer

An AI Medical Device Validation Engineer tests device software and judges whether the evidence supports its intended use. They help a company show that an AI diagnostic function is safe, effective and ready for review.

What they do

They turn intended use and requirements into verification and validation plans, then run the tests. They build test datasets and check performance across the patient groups, scanners and settings the device will meet in practice. They investigate failures and ask whether each one matters clinically. They keep every requirement, test, result and defect linked in a traceable record. They work with ML engineers, clinicians, regulatory colleagues and quality teams, and write up the evidence so an auditor or reviewer can follow it without help.

What a strong hire delivers

In the first months, a strong hire gives the company a validation plan tied to intended use and a clear view of where requirements are not yet covered. They produce a traceable test package for a device function, with failures ranked by clinical consequence. They also leave the team with a repeatable way to run validation as models and software change, and documentation that stands up to quality review.

How you can tell they are good

Ask them to walk through a validation they ran from requirements to final report. Listen for how they chose test data, set acceptance criteria and handled a failure that was hard to explain. Ask how they separate a statistical miss from a clinical risk. Strong people are specific about traceability and uncertainty. Be wary of anyone who talks only about accuracy figures.

Why it is hard to fill

Few people sit across validation, quality systems and machine learning. Most come from one side. Medical device validation engineers often lack experience with model behaviour, and ML engineers rarely know design controls or traceability. Titles vary, so searching on one label misses good people. Those who have done both are usually settled in regulated teams and need a clear reason to move.

Where to find them

Look in medical imaging companies, diagnostic device makers and digital health firms building software as a medical device. Useful titles include verification engineer, validation specialist, clinical scientist, biomedical engineer, quality engineer and ML engineer with regulated experience. Hospital physics and clinical engineering groups, and research teams that moved into product work, are also worth checking.

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 Medical Device Validation Engineer

  1. Describe a validation package you built for a software-driven device. How did you organise requirements, tests and evidence?

    A strong answer shows traceability from intended use to requirement to test to result, with clear ownership of gaps. They explain how they kept the package reviewable. Worry if they describe testing without any link back to requirements.

  2. How would you choose test data for an AI imaging function intended for use across several hospitals?

    Look for attention to scanner types, sites, patient groups, disease spread and separation from training data. They should mention bias and edge cases. Worry if they suggest one convenient dataset or random splits only.

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

Job description you can post

We are building AI-enabled functions for diagnostic and monitoring devices. We need a validation engineer to verify our software and assess whether the evidence supports its intended use. You will work closely with ML, clinical, quality and regulatory colleagues to produce evidence we can stand behind.

What you will do

  • Write verification and validation plans tied to intended use and requirements
  • Design and run tests for AI-enabled device software
  • Build and maintain test datasets that reflect real clinical conditions
  • Investigate failures and assess their clinical relevance
  • Maintain a traceable record linking requirements, tests, results and defects
  • Report validation findings clearly to technical, clinical and quality audiences
  • Re-assess evidence when models or software change

What you will bring

  • Validated device software or a comparable regulated product, shown through your own work
  • Working knowledge of quality systems, design controls and traceability
  • Experience testing machine learning or imaging functions, including performance across subgroups
  • Ability to judge failures by clinical consequence, not only statistics
  • Clear technical writing that reviewers can follow
  • Comfortable working with clinicians and ML engineers

Nice to have

  • Experience with uncertainty quantification or calibration
  • Background in medical imaging, clinical science or biomedical engineering
  • Familiarity with AI governance and assurance practices

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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