Hiring nowAI assurance, audit and standards

AI Auditor

Also called Algorithmic Auditor · AI Auditor (Third-Party) · Model Assurance Auditor

An AI Auditor independently tests whether a company's AI controls work as claimed, against an agreed scope and criteria. They give buyers, partners and regulators evidence they can trust, which makes sales and compliance conversations easier.

What they do

The auditor agrees the scope and criteria first, such as a management system standard or a legal requirement. They then build an audit programme and a control matrix. They request evidence, sample it, and test whether controls operate as described. That covers model evaluation records, data governance, change management, human oversight and incident handling. They interview engineers and risk owners, check claims against logs and test results, and rate what they find. They write findings that stand up to challenge, agree wording with the client, and track remediation until verified.

What a strong hire delivers

In the first months, a strong hire delivers a clear audit scope and criteria that the business and its stakeholders accept. They produce a control matrix mapped to the relevant standards and laws, and a first set of findings with evidence behind each one. Engineers understand what is being asked of them. Remediation items are agreed, owned and verified, so the company can show assurance work that holds up outside the building.

How you can tell they are good

Ask them to walk through a past audit from scoping to closure. Good candidates explain how they chose samples, why a control passed or failed, and what evidence was enough. They can describe a model evaluation in plain terms and say where it falls short. Worry if they recite standards without examples, accept management assertions as evidence, or cannot explain how they stayed independent.

Why it is hard to fill

Few people combine real audit discipline with enough machine learning understanding to test AI controls. Titles vary widely, so searching on the label misses most of them. Traditional auditors often lack technical depth, and technical risk people often lack audit method. Demand is rising faster than the supply of people who have done the work. Big firms, audit startups and regulators all compete for the same small group.

Where to find them

Look in IT and technology audit, assurance and quality management teams at large professional services firms. Also look at audit startups, regulators, and technical risk or model validation teams in banks and insurers. Some come from privacy, security compliance or certification bodies. The best often hold an audit background and have taught themselves ML evaluation, or the reverse.

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 Auditor

  1. Tell me about an audit where you had to define scope and criteria for a system you did not fully understand at the start.

    Strong answers show how they learned the system, agreed criteria with stakeholders and documented limits on scope. They should name what they asked engineers and how they validated their understanding. Worry if scope was simply inherited or never challenged.

  2. How would you sample evidence to test a control for monitoring model drift?

    Look for a clear population, a sampling rationale, and attention to time periods and exceptions. They should test operation, not just design. Worry if they only ask for policy documents or a single screenshot.

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

Job description you can post

We are hiring an AI Auditor to independently test our AI controls against agreed criteria. You will review evidence, challenge claims and report clearly to technical and non-technical colleagues. The work helps us show customers and regulators that our controls operate as we say they do.

What you will do

  • Agree audit scope and criteria with system and risk owners
  • Build audit programmes and control matrices
  • Request, sample and test evidence of control operation
  • Review model evaluation results and data governance records
  • Write findings with clear, supporting evidence
  • Track remediation and verify that fixes work
  • Brief leadership on audit results and open risks

What you will bring

  • Demonstrated experience running audits or assurance reviews end to end
  • Working knowledge of audit sampling and control testing
  • Enough machine learning understanding to question evaluation methods
  • Familiarity with AI management standards and AI regulation
  • Clear written reporting that engineers and executives can both use
  • Evidence of staying independent under pressure

Nice to have

  • Experience auditing against a management system standard
  • Hands-on work with model evaluation or validation
  • Experience working with regulators or certification bodies

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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