Case Study: Himalayas achieves semantic job matching at scale with Qdrant

Himalayas boosts remote job matching recall by 19 points with Qdrant

Himalayas, a remote job board and AI job search platform, faced the challenge of scaling their keyword-based search infrastructure cost-effectively while overcoming the vocabulary gap between varied job titles and descriptions. Their previous solutions, Algolia and Elasticsearch, were expensive and unable to semantically match candidates to relevant roles that used different wording. They needed a search engine that could combine semantic understanding with precise filtering. The vendor Qdrant provided its cloud service to address this.

The solution implemented was Qdrant Cloud, which enabled a hybrid retrieval pipeline combining semantic and keyword search with payload filters for location, time zone, and employment type. This provided precise, filtered results in a single query. As a result, Qdrant now handles over 25 million requests per month with a median time of 30 ms. Recent improvements to the retrieval system, powered by Qdrant, increased recall by 19 percentage points and reduced irrelevant results by roughly four times.

Himalayas