Hiring an AEO or GEO Manager in 2025

Hiring for AEO and GEO is an active, live decision for marketing leaders now, not a hypothetical one, and companies that treat it as a side project are already losing ground to the ones who staffed it properly. Traditional SEO was built to win ranked links, while AEO and GEO are built to win citation inside an answer an AI model generates on the fly, a different mechanism that calls for a different set of skills. The practical question for a hiring manager now is not whether to fill this role but how to fill it well.

Why AEO/GEO manager is becoming a dedicated function

Search traffic used to run through a predictable pipeline: a query, a page of ranked links, a click. Generative engines collapse that pipeline into a single synthesized answer, and the only way a brand shows up in it is by being the source the model chooses to cite. It is a different target, judged by a different set of signals, one that requires someone whose full attention is on winning it, which is not a tweak to existing SEO practice.

The economics of that target reward speed. When a model repeatedly treats a given source as trustworthy across a family of related prompts, it tends to keep returning to that source, and the pattern compounds the way any reinforcing system does. A brand that locks in a citation advantage now holds a position that gets harder for latecomers to dislodge with each passing quarter. Delay is not a neutral decision. It is a cost that grows over time, and it is why the companies moving fastest on this are already treating the hire as settled business, not an open debate. The question left for this piece to answer is how to hire the right person for it.

What the role owns

The AEO/GEO manager's job spans five distinct bodies of work: building and running prompt sets, tracking citation performance across multiple AI platforms, driving technical implementation with engineering, closing content gaps against competitors, and protecting the brand against inaccurate AI output. Each of those is a real job on its own. Stacked together, they add up to a role no SEO manager or content lead can carry as a side assignment, no matter how capable that person is.

Job postings already live confirm this scope. That alone is a research and iteration workload. Add the technical half of the job, schema markup, structured data, crawler directives, internal linking, and passage-level content optimization, all run jointly with a web team, and the role now touches engineering decisions an SEO manager typically has no standing to drive on their own.

Then there's the part of the job that extends past the company's own website entirely: finding the third-party sources an AI model leans on most heavily, whether that's press coverage, directories, or community discussion, and building a deliberate placement strategy around them. Finally, someone has to watch what AI models are actually saying about the brand and lead the fix when they get it wrong. None of that is a subset of SEO work. It's an adjacent discipline that happens to share some tooling with SEO, and treating it as a tack-on to an existing job description is the single most common way companies underscope the hire.

The technical skills a job description must require, not just prefer

The single most damaging mistake in drafting this job description is listing core GEO competencies under "preferred" when they belong under "required." A candidate missing them cannot do the job, full stop on the logic even if the posting is lenient on paper.

Start with the baseline Stripe sets in its own listing: five or more years building and running SEO strategy, with direct hands-on experience in answer engine optimization and generative engine optimization specifically, not adjacent familiarity. From there, the candidate needs a working understanding of how large language models actually select, retrieve, and cite material, and how that behavior differs across ChatGPT, Perplexity, Google AI Overviews, and Gemini. These platforms do not share one citation logic. On the implementation side, the candidate needs proven, hands-on ability to make a website AEO-friendly: Organization, Person, and Product schema markup, structured data, canonical issues, crawlability, and optimizing content at the passage level. Measurement is just as central to the job as the content and technical work. The role needs real fluency in dashboard tools such as Tableau, Looker, Adobe Analytics, or Google Analytics, because turning raw platform data into a report a CMO can act on is a core output of the job, not a side task. Add to that a grasp of user intent built around how people actually query AI systems, which behaves differently from the keyword-intent models that built traditional SEO.

Beyond that baseline, several skills deserve far more hiring weight than "preferred" status usually implies. Familiarity with natural language processing, vector search, and retrieval-augmented generation sits in Stripe's preferred column today, but as more AI platforms move toward RAG-based retrieval, a candidate who understands that architecture will out-perform one who doesn't, and a hiring team that treats this as optional is making a bet against where the field is heading. Round out the profile with comfort in data visualization tools like Power BI and some experience running A/B tests against AI citation outcomes specifically, and the job description stops being a wish list and starts being a real filter.

The strategic and content skills that separate a capable hire from a great one

Technical competence gets a brand onto the board. Whether it stays there, and whether the program compounds instead of stalling, comes down to a different set of skills entirely: a feel for how content needs to be built for AI citation, and a strategic sense of where a brand's authority actually lives outside its own website.

Content written to win citation in a generated answer does not read like content written to rank on a search results page or satisfy a human reader scanning for a headline. It needs the direct answer placed at the start of the passage, matching how a generative model extracts and reassembles text, a different pattern than how a person scans down a page. It needs facts delivered at a density that reads as authoritative to a model parsing for reliable signal, and it needs to cite other authoritative sources throughout, because those citation networks are part of how a model builds trust in a given entity. A candidate unaware of that will spend budget pushing the brand in the wrong direction.

The strategic half of the job runs past the company's own content. Most AI brand mentions trace back to sources the company doesn't own: press coverage, Reddit threads, directory listings, analyst notes. Those surfaces are operational inputs to a GEO program, not side projects for the brand or PR team to run independently. The strongest candidates come in with a systematic method for finding which third-party sources a given AI model leans on most heavily, and a plan for building sustained placement and citation programs on exactly those surfaces. Layer on top of all this the discipline to treat every AI platform as its own strategic puzzle rather than one undifferentiated target, because a candidate who can name the specific behavioral differences between platforms and adjust tactics accordingly is the one who can actually lead the cross-functional work, pulling engineering, content, and web teams into a single coordinated program rather than running GEO as a set of disconnected requests. That leadership capacity is also the thing a hiring manager has to test for directly. It leads straight into measurement competency.

Evaluating a candidate's grasp of cross-platform AI visibility measurement

Measurement ability is the clearest signal of whether a candidate has actually run a GEO program before, because someone who can't explain how they'd track citation performance across platforms has never had to do it under real conditions.

A strong first test in an interview is simple: ask the candidate to define AI share of voice, calculated as brand citations divided by total category citations, multiplied by one hundred, and ask them why that number has to be tracked separately on each platform. Push further: ask them to describe, concretely, how they would build a reporting cadence and a dashboard that turns raw AI visibility numbers into something a CMO or a board can actually weigh against pipeline and revenue. A candidate who has done this work before will have a specific answer, not a general one.

Beyond the open-ended questions, there's a short list of KPIs a genuinely qualified candidate should be able to name without being prompted: citation rate broken out by platform and by query type, the accuracy and distribution of sentiment inside AI-generated answers, the share of citations that point to official brand content rather than competitors or low-quality third parties, and the pair of operational metrics that matter most when something goes wrong, mean time to detect and mean time to resolve a harmful or inaccurate AI answer. A candidate fluent in all of these is demonstrating that they've run this measurement discipline for real, which sets up the next test directly: whether they understand brand risk with the same operational rigor.

Brand integrity and hallucination monitoring as a required program component

Most job descriptions written for this role today have no structured answer for what happens when an AI model gets something about the brand wrong, and that gap is the biggest blind spot in how companies are hiring for it right now. A hallucination about a brand is a predictable result of specific, identifiable gaps in how that brand's information is structured and presented across the web, and closing those gaps is squarely the AEO/GEO manager's job.

When a brand's website structure is ambiguous, its schema markup is incomplete, or its entity data conflicts across different sources on the web, a language model fills in the resulting gaps with inferred or outright fabricated information. The model, in that moment, is doing what it's built to do with input that doesn't give it a clean answer, so the fix lives in the quality of the brand's own information architecture, not in asking the model to behave differently.

A properly built prevention program needs several concrete pieces working together. A verified "Brand Facts" page should state the company's core entity information in plain, unambiguous terms. Organization, Person, and Product schema markup needs to be applied with real rigor so an AI crawler has a clean structured answer to pull from. Entity data needs to stay consistent everywhere it appears: directory listings, Wikipedia, analyst write-ups, and press coverage all need to agree with each other, because a model trained or retrieving across conflicting versions of the same fact has no reliable signal to anchor to. An /llms.txt file, a crawlable Markdown file that gives AI models a curated index of a site's most important content, gives crawlers a direct, authoritative source to pull from at the moment they're generating an answer, instead of leaving them to stitch together inconsistent signals from elsewhere. And none of this works without a live monitoring program that catches a harmful or inaccurate AI answer quickly and resolves the content gap behind it fast, the MTTD and MTTR discipline covered in the measurement section above. A candidate who treats hallucination monitoring as a PR cleanup job rather than a proactive content and schema discipline has misunderstood what the role is for.

What the market is paying for this role

Pay for this role varies widely right now, and where a given company lands in that range says as much about how it's scoped the job as it does about local market conditions. The number should reflect the seniority and strategic reach the company actually wants this person to carry, not whatever the outgoing SEO manager happened to earn.

The postings available show just how wide that band already is. A temporary GEO/AEO Manager role at Monks, based in Boston, is paying $53 an hour. The range between those two postings is substantial, and it reflects a real difference in scope rather than a simple geographic or market quirk: a temporary tactical hire is at one end, and a senior strategic role with oversight of other strategists and a book of client business is at the other. A company writing this job description for the first time needs to decide, deliberately, which end of that range its own version of the role actually belongs to, because the scope defined in the earlier sections of this piece, prompt strategy, cross-platform measurement, technical implementation, off-property influence, and hallucination prevention, is senior work. Pricing it like a junior SEO hire is the fastest way to lose a qualified candidate to a competitor who scoped the job correctly from the start.