An embedding is a list of numbers that captures the meaning of a piece of text, so texts with similar meaning sit close together. Embeddings let a tool match a resume to a job, or find similar candidates, even when the two use different words for the same skill.
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A list of numbers that captures the meaning of a piece of text, so texts with similar meaning sit close together. Embeddings let a tool match a resume to a job even when they use different words.
An embedding model reads a sentence, a resume or a job description and turns it into a long string of numbers. Two pieces of text about similar things, such as 'payroll specialist' and 'payroll administrator', end up with similar numbers. The tool can then measure how close any two texts are.
This is what powers semantic search in many ATS, CRM and sourcing tools. Instead of needing an exact keyword, you can search for 'people who have built data pipelines in fintech' and get profiles that describe that work in their own words. Embeddings also help AI assistants find the right records to ground an answer.
Closeness in meaning is not the same as fit for the job. Embeddings can miss must have requirements, overweight buzzwords and carry patterns from the data they were trained on, so treat match scores as a starting point.
Why it matters to recruiters
Recruiters meet embeddings every time a tool shows a match score or 'similar candidates' list. Knowing how they work helps you trust them where they shine, such as finding people your Boolean string missed, and question them where they fail, such as ranking a candidate highly because their resume uses the right vocabulary.
An example from a recruiting desk
A sourcer searches her ATS for 'site reliability engineer' using keywords and gets 40 results. The semantic search, which uses embeddings, returns 95, including people whose resumes say 'production operations' and 'platform engineering'. She reviews the extra 55, finds eight strong fits, and keeps her keyword search as a cross check for hard requirements like a security clearance.
Tips that help
Use semantic search to widen a pool, then filter on hard requirements yourself.
Ask vendors what drives a match score and whether it has been tested for bias.
Combine embedding search with Boolean search rather than replacing one with the other.
Write clear, specific job descriptions, since vague ones produce vague matches.
Mistakes to avoid
Treating a high match score as proof that a candidate meets the requirements.
Rejecting candidates automatically because their score falls below a cut off.
Frequently asked questions
Are embeddings the same as keyword matching?
No. Keyword matching needs the same words in both texts. Embeddings compare meaning, so 'talent acquisition' and 'recruiting' can match. Many tools use both together to balance recall and precision.
Do embeddings store the original resume?
An embedding is a numeric summary, not the text itself, but tools usually store it alongside the original record. Treat both as candidate data under your privacy and retention rules.
Can embedding match scores be biased?
Yes. If the training data or your past hires reflect bias, the scores can repeat it. Check outcomes across groups and keep a person reviewing who moves forward.
