Hiring nowAI assurance, audit and standards

AI Assurance Specialist

Also called AI Assurance Analyst

An AI Assurance Specialist independently tests the claims made about an AI system and the evidence behind them. They help a company show customers, regulators and its own board that what it says about its AI is true.

What they do

They take a claim, such as a system being safe for a stated use, and ask what evidence supports it. They read evaluation reports, test results and control documentation. They check that each claim links to evidence that is credible, current and relevant. They map claims against control matrices and audit programmes, and keep the evidence repository in order. They interview engineers and product owners, find gaps, and push back where evidence is thin. They write up what they found and what remains uncertain, in language non-specialists can act on.

What a strong hire delivers

In the first months, a strong hire gives the company a clear view of which claims about its AI are well supported and which are not. They set up a repeatable way to trace claims to evidence. They produce assurance assessments that leadership and external parties can rely on, each with a plain statement of residual uncertainty. Engineering teams learn what good evidence looks like and start supplying it earlier.

How you can tell they are good

Ask them to take a claim you make about your system and show how they would test it. Strong people ask what the claim covers, who relies on it and what would change their mind. Ask for a past assessment, sanitised, and how they stated what remained unknown. Be wary of anyone who treats a passed test as proof, or who cannot say what evidence would be too weak.

Why it is hard to fill

The title is new and used inconsistently, so searching by it misses most good people. Candidates tend to be strong in one half only: seasoned auditors who do not understand how models are evaluated, or technical evaluators who have never had to defend a finding independently. Few have done both. Companies also ask for someone senior enough to challenge engineers, which narrows the pool further.

Where to find them

Many come from internal audit, external assurance, quality management and technical risk teams, often in regulated sectors such as finance, health or safety-critical engineering. Others move across from model evaluation, trust and safety, or governance and compliance roles inside AI companies. Consultancies and assurance firms building AI practices are another source. Look for audit discipline paired with real curiosity about how models behave.

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 Assurance Specialist

  1. Describe a time you challenged a claim and found the evidence did not support it. What did you do next?

    A strong answer names the claim, the gap and how they raised it with the owner. They show they stayed independent while remaining constructive. Worry if they only describe agreeing with teams or never found a real gap.

  2. How do you decide whether evidence for an AI system's performance is credible?

    Good answers cover relevance to the stated use, test design, data representativeness, independence of the evaluator and how current the evidence is. Worry about answers that rely only on headline scores or on volume of documents.

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

Job description you can post

We build and deploy AI systems and need someone to independently test what we say about them. You will review our claims, examine the evidence behind them and tell us plainly where confidence is earned and where it is not. You will work closely with engineering, product and leadership.

What you will do

  • Review claims about our AI systems and trace each to supporting evidence
  • Assess whether evidence is credible, relevant and current
  • Maintain control matrices, audit programmes and the evidence repository
  • Interview engineers and product owners to find gaps
  • Write assurance assessments with clear residual-uncertainty statements
  • Advise teams on what good evidence looks like
  • Report findings to leadership in plain language

What you will bring

  • Demonstrated assurance, audit, quality or technical risk work, with examples you can describe
  • Experience reviewing evidence and challenging claims independently
  • Working understanding of how AI models are evaluated
  • Familiarity with AI risk management and governance approaches
  • Clear written and spoken technical communication
  • Comfort holding a position with senior engineers

Nice to have

  • Experience in a regulated sector
  • Hands-on involvement in model evaluation
  • Experience preparing for external audits or reviews

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

Skills that matter

  • AI governance and assuranceAdvancedAssign accountability and verify lifecycle controls
  • AI risk managementAdvancedIdentify, assess, treat and monitor AI risks
  • Model evaluationWorkingMeasure capability and failure modes credibly
  • Technical communicationWorkingExplain evidence and limitations to relevant readers

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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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