CareerBuilder boosts AI expertise with acquisition of Textkernel

With the acquisition of the semantic search company, CareerBuilder now has more than 200 data scientists, engineers and AI specialists.
Written by Stephanie Condon, Senior Writer on

CareerBuilder on Wednesday announced it's acquired Textkernel, a Netherlands-based firm that uses AI and machine learning technologies for matching people with jobs. With the acquisition, CareerBuilder now has a team of more than 200 data scientists, engineers and AI specialists working on its HR technology.

Textkernel, founded in 2001, specializes in the semantic understanding of documents and queries. Its technology is already fully integrated into CareerBuilder's Talent Discovery Platform, which finds suitable job candidates by connecting patterns in skills, geography, experience and job progression. Textkernel technology is also being integrated into CareerBuilder's upcoming Talent Discovery Companion App.

CareerBuilder plans to integrate Textkernel's semantic search capabilities into tools for both job seekers and employers.

"In our nearly 25 years of experience, we've learned that recruiters speak one language and candidates speak another," CEO Irina Novoselsky said in a statement. "We are building and implementing technologies that close this gap for both sides of our marketplace, and improve every stage of the hiring process."

The acquisition builds on five years of investments in AI, CareerBuilder said. It's applied AI to a range of products and services, such as employment screening and applicant tracking.

Several of CareerBuilder's competitors are also busy building AI capabilities into their HR and job recruitment tools. For instance, in October, the online recruiting marketplace ZipRecruiter raised $156 million in Series B venture funding with plans to expand its machine learning and AI infrastructure. In 2017, Google rolled out Google for Jobs, an initiative leveraging machine learning to bring job seekers and employers together via Google Search.

Editorial standards