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Recruitment startup Eightfold AI secures $220 million in Series E funding

by Ben Wodecki
Article ImageCalifornia-based recruitment startup Eightfold AI has raised $220 million in Series E funding, led by SoftBank Vision Fund 2.

The startup provides a talent acquisition platform that filters through potential candidate resumes using artificial intelligence.

The latest funding round brings Eightfold’s total capital raised to over $410 million – with the startup closing a similarly lucrative Series D round last October.

The five-year old startup is now valued at around $2 billion – with the latest funding round doubling its valuation.

"Transforming HR and global talent further unlocks trillions of dollars worth of human potential. SoftBank shares our bold vision, and we are excited to welcome them as our partner,” Ashutosh Garg, founder and CEO of Eightfold, said.

Modernizing recruitment via AI

Based in Mountain View, Eightfold boasts clients in over 100 countries – with its Talent Intelligence Platform available in several languages.

The startup said the latest cash injection will be used to further accelerate its expansion efforts, as well as hiring more data engineers and scientists.

Investing alongside SoftBank Vision Fund 2 were General Catalyst, Capital One Ventures, Foundation Capital, IVP, and Lightspeed Venture Partners — all of whom had invested in prior rounds.

The startup claims that current HR systems “were designed to address issues from a previous era, and they have failed to keep pace with the changing nature of work and the workforce.”

Candidates using Eightfold’s platform would upload their resume and the system would then suggest the most relevant role in real-time.

“What this does is, it reduces the drop-off rate. And our clients see more applications — and field more diverse applications,” Garg told TechCrunch.

Under the recently unveiled draft EU legal framework on AI, AI-based recruitment tools would be considered "high-risk," meaning such products would be subject to “strict obligations” before they can be put on the market.

Such obligations include risk assessments, ‘appropriate’ human oversight measures, and high levels of security and quality of datasets.


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