What an AI job-match score actually measures
Different platforms report "match scores" but they often mean very different things. Here is what semantic AI matching actually does under the hood.
ApplyTOP · April 29, 2026
You've seen the "92% match" badge on job boards. What does that number actually mean? In most cases — less than you'd expect. Let's unpack the difference between keyword-based scoring and semantic matching.
Keyword overlap is the old approach
The simplest match score counts how many keywords from the job description appear in your CV (or vice-versa). This breaks down fast: a CV mentioning "PostgreSQL" doesn't match a JD asking for "Postgres", and "Senior Software Engineer" doesn't match "Backend Developer" even if they're the same role.
What semantic matching does instead
Semantic models embed your CV and the job description into the same high-dimensional vector space and compute the cosine similarity between them. ApplyTOP uses the all-MiniLM-L6-v2 model to produce 384-dimensional embeddings. Two pieces of text with similar meaning end up near each other in that space, even if they share no exact words.
Why this matters
Semantic matching catches the cases keyword scoring misses:
- "Designed event-driven microservices" matches "experience with async messaging architectures".
- "Led growth experiments" matches "owned A/B testing pipeline".
- "Senior Backend Engineer (5 years)" matches "Server-side Developer (mid-to-senior)".
The result is a 0–100 score that captures genuine fit, not just keyword density. It's the same family of techniques used in modern semantic search engines.