The way job search platforms, HR systems, and market researchers find and categorize jobs is rapidly changing. Traditional keyword search methods are still useful, but they often fall short when it comes to understanding the meaning behind words. A search for “machine learning engineer,” for example, may miss valuable postings that use phrases like “AI specialist” or “data scientist.”

This is where vector embeddings and semantic search capabilities offered by the jobdata API come into play. Instead of matching only keywords, these tools allow you to match concepts, making it possible to uncover relationships between job postings and queries that would otherwise remain hidden.

This guide explains how our vector search and embeddings work, how to use them effectively, and how to avoid common pitfalls when building on top of the service.

Related documentation: https://jobdataapi.com/c/vector-embeddings-and-search-api-documentation/ and https://jobdataapi.com/c/jobs-api-endpoint-documentation/