Hiring nowResponsible AI governance and risk

Responsible AI Engineer

Also called Fairness/Trust Engineer

A Responsible AI Engineer builds bias testing, transparency and provenance into AI systems. They turn risk policy into working controls, so the company can show its AI is fair, explainable and accountable.

What they do

They take a policy statement and make it testable. That means setting up fairness checks on models and datasets, running impact assessments with product teams, and building the records that show where data and model outputs came from. They write evaluation code, review results, and flag where a system falls short. They work with legal, risk and audit colleagues who need clear evidence, and with engineers who need clear fixes. They also document decisions, track open issues to closure, and explain trade-offs in plain language to people who sign off on release.

What a strong hire delivers

In the first months, a strong hire maps which AI systems the company runs and ranks them by risk. They put a repeatable bias and fairness test into at least one live release process. They set up a simple way to record data lineage and model decisions. They give leadership a short, honest view of the main gaps and what to fix first.

How you can tell they are good

Ask them to walk through a system where they found a fairness or transparency problem. Listen for the metric they chose and why, what they changed, and what they could not fix. Ask them to explain a technical finding to a non-technical approver. Be wary of anyone who talks only about principles, or only about tooling, and cannot connect the two.

Why it is hard to fill

The title is used loosely, so CVs are hard to read. Some candidates are strong ML engineers with little governance experience. Others come from risk, audit or compliance and cannot build or run tests. You need both halves, and few people have them. Demand is high, especially from large enterprises, so good people are rarely on the market for long.

Where to find them

Look at ML engineers who moved into fairness or evaluation work, and at risk, compliance, audit and technology governance people who learned the technical side. Titles include trust engineer, fairness engineer, AI assurance analyst and model risk specialist. Many sit in regulated enterprises such as banks, insurers and healthcare, or in the trust teams of larger technology firms.

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 Responsible AI Engineer

  1. Tell me about a time you found bias in a model or dataset. What did you do next?

    A strong answer names the metric, the affected group, the cause and the fix, and admits what stayed unresolved. Worry if they describe only general principles, or claim the problem was fully solved.

  2. How do you choose a fairness metric when several of them conflict?

    They should explain that metrics trade off, tie the choice to the use case and harm, and involve stakeholders. Worry about one favourite metric applied everywhere without reasoning.

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

Job description you can post

We are hiring a Responsible AI Engineer to make sure our AI systems are fair, transparent and accountable. You will turn risk policy into tests, records and controls that teams actually use, and give leaders clear evidence of where we stand.

What you will do

  • Design and run bias and fairness tests on models and datasets
  • Carry out impact assessments with product and engineering teams
  • Build records of data lineage and model provenance
  • Turn governance policy into practical, testable controls
  • Report risks and gaps clearly to leadership, risk and audit colleagues
  • Track issues through to resolution and document decisions

What you will bring

  • Shown work testing AI systems for bias or harm, with examples you can discuss
  • Ability to write evaluation code and read model outputs critically
  • Experience applying risk, assurance or governance methods to technical systems
  • Clear written and spoken explanation of technical findings to non-specialists
  • Sound judgement on trade-offs between fairness measures and business needs

Nice to have

  • Background in risk, audit or compliance in a regulated setting
  • Experience with transparency or explainability methods
  • Familiarity with emerging AI regulation and assurance standards

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

Skills that matter

  • AI risk managementWorkingIdentify, assess, treat and monitor AI risks
  • AI governance and assuranceWorkingAssign accountability and verify lifecycle controls
  • Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts
  • 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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