Who is a Search Engineer and Why Does Their Role Matter Now?

A search engineer is a person who turns your brand into the best answer, not just a ranked result. For years, marketing fought for three things: budget, attention, and clicks. Now, there is a fourth fight that decides the first three.

In the US, AI searches reached 5.6% of desktop search traffic by June 2025, more than double year-over-year, and early adopters are already routing a far larger share of searches to AI tools. At the same time, AI-driven search advertising is projected to surge to nearly $26 billion in the US by 2029. That is a structural shift in how demand gets discovered, evaluated, and purchased.

That is why the search engineer suddenly matters to a CMO. In an AI-mediated world, the winners are not the loudest brands. They are the most recognisable brands.

What exactly does a Search Engineer do?

A search engineer builds and improves the systems that decide what gets seen when someone searches, not only on Google, but across every search surface that now influences buying decisions: site search, marketplace search, app search, internal knowledge search, CRM search, and increasingly AI assistants that summarize instead of listing links.

A mature search engineering scope typically includes four layers - 

1) Retrieval foundation
How content is stored, indexed, and fetched. This includes crawling or ingestion, parsing, deduplication, metadata enrichment, and building indexes for both keyword and semantic retrieval.

2) Ranking and relevance
How results are ordered. This includes scoring models, query understanding, learning-to-rank, hybrid retrieval, and reranking. It also includes tuning for different intents, such as exploratory research, product comparison, troubleshooting, compliance queries, and procurement-style searches.

3) Experience surfaces
Autocomplete, facets, filters, synonyms, “people also search”, related items, and recommendations. This is where search stops being a utility and becomes a lever for conversion.

4) Measurement and experimentation
Offline evaluation plus online experimentation. Search quality is a living system, so continuous testing is the real job.

The common mistake is to treat this as IT plumbing. It is not. Search engineering is revenue plumbing.

Sources used for role scope and responsibilities include search-engineering role definitions and relevance-engineering explanations. 

Why is this role suddenly mission-critical in 2025?

What changed in how buyers find information?

Buyers now interact with answer engines, not only search engines. AI interfaces compress the funnel by responding with a single synthesized answer more often than a page of links. When the UI gives fewer slots for visibility, the penalty for being second best becomes brutal.

This is not a theory. Independent reporting and market data show rapid growth in AI-assisted search usage and a reshaping of traffic patterns. 

Search itself became multi-modal and multi-system

Modern search is not one algorithm. It is a stack of systems that interpret meaning, fight spam, and rank at the page level using many signals. Search engineers translate these realities into a system your brand can consistently win in.

AI retrieval is now the buying gatekeeper

Even when users do not “search a website,” they still query a system. CRM search, enterprise search, product catalogue search, support knowledge search. AI assistants sit on top of these and retrieve content using both keywords and vectors.

Meta’s work on billion-scale similarity search shows why vector search has become a core capability: traditional query search struggles with multimedia and semantic similarity at scale. This is the technical undercurrent driving how answers are pulled. 

Who owns visibility now: Search Engineer vs SEO vs Data Scientist?

In most B2B teams, these three roles are lumped into a single bucket called search. That is exactly how visibility programs stall. Not because people are not smart, but because the ownership is unclear. The moment AI answers started shaping buyer decisions, this confusion stopped being a process issue and became a revenue issue. 

Search engineering controls how discovery systems retrieve and rank information across surfaces. Data science strengthens measurement and models, but typically does not own end-to-end relevance outcomes. 

• SEO typically optimizes content and technical signals to improve visibility in external search engines.
  
  
• Search engineering builds the retrieval and ranking systems themselves and tunes discovery quality across surfaces.
  
  
• Data science supports measurement, modelling, and experimentation, but does not usually own the system's end-to-end behaviour.

If you are a CMO, the practical difference is simple: SEO influences inputs, search engineering governs how the machine selects and orders outputs.

This is why search engineering starts to resemble a strategic function, not a support function, in AI search conditions.

Search Engineer responsibilities that directly impact the pipeline

1) Index design and content ingestion

If your content is not ingested correctly, it does not exist to the machine. Search engineers define ingestion pipelines and the metadata schema that makes content retrievable. For CMOs, this touches product naming consistency, entity fields, vertical pages, and knowledge base hygiene.

2) Query understanding and intent mapping

The system has to interpret “best procurement software for mid-market manufacturing” differently from “procurement software pricing” and differently from “procurement software implementation checklist.” Search engineers support query parsing, intent classification, synonyms, and embeddings that improve match quality.

3) Hybrid retrieval and reranking

Keyword search is fast and precise when users know the words. Vector search is powerful when users describe meaning. Modern systems combine both, then rerank with smarter models.

This is already mainstream in enterprise search systems, where different query types have different scoring and relevance layers. 

4) Relevance tuning and guidelines

Relevance does not mean “most popular.” It means “most helpful for this intent.” Guardrails prevent failure modes like outdated content, hallucination amplification, and internal policy violations in AI-powered interfaces.

5) Search quality measurement and online experiments

You do not improve what you do not measure. Search engineering uses retrieval metrics plus A/B tests to prove business outcomes.

What should CMOs demand from search engineering metrics?

A CMO does not need to memorize formulas. You need to ensure the metrics ladder connects to revenue.

Below are the core relevance metrics and what they reveal.

• Precision answers: of what we showed, how much was relevant
  
  
• Recall answers: of what was relevant, how much we found
  
  
• MRR answers: how fast the user sees a correct answer near the top
  
  
• nDCG answers: how good the ordering is when relevance is graded, not binary
  
  
• MAP answers: how consistently we rank relevant results early across queries

These are standard IR evaluation metrics used to assess ranking and retrieval systems.

What does a modern Search Engineer toolkit look like in 2025?

A modern search engine toolkit is a controlled system that turns intent into retrieval, retrieval into ranking, and ranking into revenue.

1) Retrieval layer: hybrid search that blends keyword and vectors

This is where candidates are found fast and at scale.

• Lexical retrieval: inverted indexes, BM25 style scoring
  
  
• Vector retrieval: kNN search on embeddings for semantic matching
  
  
• Hybrid fusion: combining lexical and vector result sets using techniques like Reciprocal Rank Fusion and score normalization
  
  

2) Reranking layer: semantic rerankers that fix the top of the list

First stage retrieval is about recall. Reranking is about precision.

• Semantic reranking components that reorder the top results using language models
  
  
• Field weighting and business rules applied after semantic reranking for the final ordering
  
  

3) Embeddings and vector operations: the semantic backbone

This is how meaning is represented, stored, and searched.

• Embedding generation pipelines
  
  
• Vector indexes and compression strategies for speed and cost
  
  
• Similarity search libraries for large-scale vector workloads
  
  

4) Relevance tuning controls: the knobs that make search commercially useful

This is how you align the machine with business truth.

• Scoring profiles for boosting based on freshness, authority, business priority, geography, or product margin
  
  
• Weighting text vs vector contributions in hybrid queries
  
  
• Structured ranking controls that are explainable to stakeholders
  
  

5) Evaluation and experimentation: the proof engine

This is the discipline that prevents search from turning into opinion wars.

• Offline relevance evaluation using labeled query sets and metrics like nDCG and MRR
  
  
• Online A B testing tied to funnel events like qualified demo starts, content-assisted MQLs, and support ticket deflection
  
  

6) Observability and governance: performance, freshness, and safety

This is how you keep quality stable as content grows.

• Latency monitoring and query failure tracking
  
  
• Freshness and indexing lag monitoring
  
  
• Guidelines for query classes that can trigger risky answers, outdated policies, or compliance issues

The big idea for CMOs: discovery is now a system, not a channel. Your content strategy becomes an input to that system, and the search engineer ensures it is machine-readable, retrievable, and rankable.

How does this role change content strategy and E E A T outcomes?

Most B2B content is still written like a brochure and later optimized like an SEO task. In AI search, it has to be engineered from day one to be retrievable and trusted.

Search engineers help marketing teams create content that is:

• Entity complete: clear definitions, relationships, and attributes
  
  
• Structured: consistent headings, schema-aligned fields, and clean metadata
  
  
• Chunkable: answer blocks that can be retrieved independently
  
  
• Freshness aware: lifecycle management and deprecation signals
  
  
• Traceable: internal source truth mapping so AI outputs stay compliant

Google’s own documentation shows ranking systems are designed to rank pages using a variety of signals and systems. That puts pressure on clarity, the fulfilment of useful intent, and quality signals, which is where engineering and marketing must collaborate.

Where do most companies get search engineering wrong?

There are certain areas where brands overlook and ignore the 

1. Treating search as a UI feature
   Search is a product. It needs ownership, a roadmap, and governance.
   
   
2. Optimizing for clicks instead of answers
   In AI search, your success metric can be “being cited” or “being the chosen answer,” not only clicks.
   
   
3. No relevance benchmarks
   If you do not define relevance for your intents, you cannot tune ranking.
   
   
4. Content sprawl without entity control
   Duplicate pages, inconsistent naming, stale PDFs, and orphaned landing pages poison retrieval quality.
   
   
5. No feedback loop between marketing and engineering
   Marketing sees content. Engineering sees queries. The business needs both views stitched together.
   
   

What does hiring a Search Engineer signal about a CMO’s strategy?

It tells the market you understand what is happening: search is becoming an execution layer inside AI systems, not just “SEO rankings.” The CMO who pairs brand storytelling with retrieval engineering will outperform the CMO who scales content alone.

In 2025, you are not only competing for attention. You are competing for machine selection.

Comparing Search Engine, SEO & Relevance Engineering Outcomes

The table shows the difference between influencing the inputs to search engines, building the search system itself, and the blended model needed to win across AI-driven discovery.

Why Search Engineering Is the CMO’s Advantage in AI First Discovery?

Search engineering is no longer a backend function. It is the control layer that decides whether your brand becomes the answer or stays invisible.

Build discovery like a system, not a channel. Align content, metadata, and authority signals with how retrieval and ranking actually work. Then measure it the way you measure revenue, with relevance metrics tied to pipeline outcomes.

The brands that win the next cycle will not publish more. They will be easier to retrieve, safer to recommend, and consistently ranked for the intents that drive buying.