What is Vector Embeddings?

Vector embeddings are numerical representations of text (resumes, job descriptions) in a high-dimensional mathematical space. They capture semantic relationships, allowing AI systems to calculate the 'distance' or similarity between a candidate's profile and a job requirement, identifying matches that are conceptually related even without shared keywords.

Vector embeddings are numerical representations of text in a high-dimensional space that capture semantic relationships between words and concepts. In recruitment, this enables AI systems to compare resumes, job descriptions, and skills lists by measuring geometric similarity rather than relying on brittle keyword or synonym lists.

Why This Matters for Recruitment

Traditional matching depended on manually curated synonym tables (e.g., mapping “JavaScript” to “Node.js”), which is limited and hard to maintain. Embedding-based matching instead learns relationships from large-scale language data, so it understands that:

• “VP of Talent” ≈ “Head of People” (functionally similar leadership roles)
• A Java developer with Spring Boot experience is relevant to a search for “backend web developer” even if the exact words don’t overlap

At scale, this difference is critical: when screening hundreds of resumes per role, embeddings can surface a smaller set of genuinely qualified candidates instead of a large pool of marginally relevant ones.

How Embeddings Are Generated in TuraHire

A specialized embedding model converts each text snippet into a vector of typically 768–1,024 dimensions, where each dimension encodes latent linguistic and semantic features. TuraHire uses Voyage AI’s voyage-3.5 model, tuned for professional and technical language.

For each candidate:

• Skills
• Experience descriptions