Hiring nowPrivacy-preserving and confidential AI

AI Privacy Engineer

Also called GenAI Privacy Engineer

An AI Privacy Engineer builds consent, minimisation and access controls into AI workflows, so sensitive data is not exposed in training or inference. It matters because one disclosure incident can cost customer trust and deals.

What they do

They map how personal and sensitive data moves through training, fine-tuning, retrieval and inference, then find where it can leak. They write privacy designs, set minimisation and retention rules, and build access and consent controls into pipelines. They run privacy attack suites against models to test for memorisation and extraction. They review new features before launch, work with security and legal on data-use questions, and decide where confidential compute or cryptographic methods are worth the cost. They also write down what was tested and what remains open.

What a strong hire delivers

In the first months, a strong hire produces a current data-flow map for the main AI workflows and a ranked list of exposure risks. They put validated controls on the highest risks, set up a repeatable privacy review for new features, and run a first round of attack testing with clear findings. Engineers and legal both know what the rules are and how to follow them.

How you can tell they are good

Ask them to walk through a data flow they reviewed and explain what they changed. Good candidates name specific leak paths, such as logs, embeddings, prompts and retrieval stores, and show how they tested that a control worked. They can explain trade-offs between privacy, model quality and cost without hiding behind policy language. Be wary of people who only describe compliance paperwork.

Why it is hard to fill

Few people have done this on AI systems specifically. Most come from classic privacy engineering, security, cryptography or ML, and each has a gap. Privacy engineers may not know model behaviour. ML engineers may not know data protection duties. Titles vary, and GenAI Privacy Engineer is rare, so job title searches miss good people. We usually search by the work done, not the label.

Where to find them

Look at privacy engineers in larger software and data companies, security engineers who have moved into data protection, applied cryptographers, and ML engineers who have worked on memorisation or differential privacy. Some come from regulated sectors such as health and finance, where data handling is tightly controlled. Research groups and model providers also produce people with the right mix.

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 Privacy Engineer

  1. Tell us about a data flow in an ML or AI system you reviewed. What risks did you find and what changed?

    A strong answer names concrete exposure points, the controls added and how they were checked. It shows ownership of the outcome. Worry if the answer stays at policy level or lists risks with no fixes.

  2. How would you test whether a fine-tuned model has memorised sensitive training data?

    Good answers cover extraction and membership inference tests, canaries, and how to read results with care. They note the limits of testing. Worry if they claim a single test proves safety.

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

Job description you can post

We are hiring an AI Privacy Engineer to make sure sensitive data is handled safely across our AI systems. You will design and validate controls for training and inference, and work closely with engineering, security and legal to keep data use sound as we build.

What you will do

  • Map data flows across training, fine-tuning, retrieval and inference
  • Design consent, minimisation, retention and access controls for AI workflows
  • Run privacy attack testing on models and report findings clearly
  • Review new features for privacy risk before release
  • Validate that controls work and keep evidence of testing
  • Support investigation and follow-up of disclosure incidents
  • Advise teams on when to use confidential compute or cryptographic methods

What you will bring

  • Demonstrated privacy-by-design work on a real data-heavy system
  • Experience reviewing data flows and finding leak paths
  • Working knowledge of how models can memorise or expose data
  • Solid security engineering fundamentals
  • Ability to write code and build controls into pipelines
  • Clear written communication with technical and legal colleagues

Nice to have

  • Experience with confidential compute or cryptographic libraries
  • Hands-on use of privacy attack suites
  • Background in a regulated sector

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

Skills that matter

  • Privacy engineeringAdvancedLimit disclosure and unauthorised personal-data use
  • Security engineeringAdvancedProtect systems, identities and sensitive assets
  • Formal verificationWorkingProve stated properties under explicit assumptions
  • Data quality and provenanceWorkingTrack dataset origin, validity and transformations

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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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