Semantic Search vs Keyword Matching in Hiring
Why keyword filters miss great candidates, how vector embeddings fix it, and where a hybrid approach beats both.
Keyword matching has quietly cost companies great hires for decades. A candidate who wrote "built ML pipelines" gets filtered out of a search for "machine learning" — same meaning, different words.
The problem with keywords
Keyword search is lexical: it matches strings, not meaning. It can't tell that "RAG" and "retrieval-augmented generation" are the same, or that "led a team" implies leadership.
What embeddings change
Semantic search embeds text into a vector space where meaning is geometry. Similar concepts land near each other, so "built ML pipelines" and "machine learning engineering" score as related — even with zero shared keywords.
Why hybrid wins
Pure semantic search can over-match. In practice, the best ranking blends:
- Semantic similarity — the meaning layer.
- Structured signals — skills, seniority, recency.
- Explainability — a rationale a recruiter can trust.
That hybrid is exactly what powers candidate ranking in TalentPike.
This is a placeholder draft — the full article is coming soon.