Vector Embeddings Outperform Keywords in Semantic Job Matching

This work demonstrates how vector embeddings enable context-aware job discovery that transcends keyword matching, with the tool correctly ranking unrelated roles last regardless of keyword overlap. For freelancers overwhelmed by irrelevant listings, this represents a fundamental shift from keyword-based filtering to semantic understanding that considers professional context and portfolio fit. The practical results show that embedding-based systems capture nuanced career alignment that traditional search cannot achieve.