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Generative Engine Optimisation Specialist

Also called Generative Engine Optimisation (GEO) Specialist · AI Search Optimisation · AEO Specialist

A Generative Engine Optimisation Specialist improves how often and how accurately a brand appears in AI assistants' answers. It matters because more buying decisions now start with an assistant rather than a list of links.

What they do

They work out how AI assistants talk about the brand and its competitors. They build sets of realistic customer questions, run them repeatedly across assistants, and record whether the brand appears, how it is described and what sources are cited. They then change what the company publishes and structures: product data, comparison pages, reviews, expert content and third party mentions. They test whether changes shift results, separate real movement from noise, and report to marketing, product and commercial teams in plain terms. They also flag wrong or unfair claims about the brand.

What a strong hire delivers

In the first months, a strong hire delivers a baseline of current brand visibility across the main assistants and customer questions. They produce a ranked list of gaps with clear causes, a repeatable measurement method the team trusts, and a handful of tested changes with honest results. They also set up a regular report that links visibility to commercial outcomes, and a shared view of what to fix first.

How you can tell they are good

Ask them to show how they measured visibility and how they knew a change worked. Strong people talk about sampling, variance and repeat runs, not single screenshots. They can explain why assistants cite some sources and not others. They are honest about what cannot be controlled. Be wary of anyone promising guaranteed rankings or treating this as old SEO with a new label.

Why it is hard to fill

The title is new and the work sits between several disciplines. Candidates are often strong at SEO or content but weak on statistics, or good analysts with little feel for how assistants retrieve and cite sources. Job titles vary a lot, so searching on the label misses many capable people. Few have measured results that go beyond anecdotes, which makes the good ones easy to overlook.

Where to find them

Look at SEO leads, content strategists and ecommerce growth managers who have moved into measurement. Commercial analysts, marketing scientists and applied ML people with an interest in search also fit. Many sit in brands with large product catalogues and in digital, performance or content agencies. Some come from search quality, information retrieval or conversational product teams.

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 Generative Engine Optimisation Specialist

  1. How would you measure whether our brand appears in AI assistant answers for the questions our customers ask?

    Covers building a realistic question set, repeated runs, multiple assistants, tracking mentions, sentiment and citations, and handling variation between runs. Worry if they rely on a few manual searches or one tool output without understanding it.

  2. Tell us about a change you made to content or data that altered how a brand was described. What happened?

    A specific example with a baseline, a change, a result and caveats. They should admit what they could not prove. Worry about vague claims, unattributed wins or results measured once.

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

Job description you can post

We want a Generative Engine Optimisation Specialist to improve how our brand shows up in AI assistants' answers. Our customers increasingly ask assistants what to buy, and we need to be present, accurate and fairly described. You will measure, test and improve our visibility, working with marketing, product and data teams.

What you will do

  • Build and maintain question sets that reflect real customer intent
  • Measure brand presence, description and citations across assistants
  • Diagnose why competitors are cited and we are not
  • Recommend and test changes to content, product data and external mentions
  • Report results and uncertainty clearly to non-specialists
  • Flag inaccurate or unfair claims about our brand and drive fixes

What you will bring

  • Demonstrated work measuring visibility or performance with sound statistical thinking
  • Hands-on experience in SEO, content strategy, ecommerce or marketing analytics
  • Understanding of how assistants retrieve, rank and cite sources
  • Ability to design tests and separate signal from noise
  • Clear written communication for commercial audiences
  • Commercial sense about linking visibility to revenue

Nice to have

  • Experience with structured product data at scale
  • Background in information retrieval or applied ML
  • Experience assessing fairness or accuracy of automated outputs

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

Skills that matter

  • Information retrievalWorkingRetrieve and rank relevant documents or entities
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Commercial and financial analysisWorkingAssess unit economics and value realisation
  • Fairness and impact assessmentWorkingAssess unequal errors, access and downstream impacts

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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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