Job Skills Matching

An enterprise staffing platform was struggling with matching: connecting candidates to jobs at scale. Their system relied on keyword matching — if a resume and listing shared exact terms, it was a match. Everything else was missed.

Qualified candidates were overlooked because they described skills differently. A candidate with "Agile methodology, sprint planning, stakeholder coordination" wouldn't match "project management." Recruiters spent hours manually reviewing candidate pools.

The platform needed a system that understood skills semantically — recognizing related competencies, transferable experience, and adjacent skills.

We built a semantic skills matching engine that maps both job requirements and candidate skills into a shared embedding space where similarity is measured by meaning, not string matching.

The architecture has three layers: a skills extraction pipeline parsing both resumes and job descriptions; a matching engine computing relevance across multiple dimensions — direct skill match, adjacent skill coverage, experience level, and domain relevance; and a ranking layer letting recruiters tune weights per role.

We trained the model on historical placement data, so it learned from actual successful placements — what candidate profiles actually succeeded in what types of roles.