TripleByte raises $35m to use ML to find non-traditionally skilled engineers work

The Series B financing round will be used to help the San Francisco-based startup expand into Los Angeles, New York, Seattle and Boston


Triplebyte, a San Francisco-based startup which uses machine learning (ML) to match up available, but non-traditionally skilled engineers with open jobs, has raised $35m in a Series B financing round led by YC Continuity, bringing the total amount the company has raised to $48.1m.

Other investors include Founders Fund's Brian Singerman, Caffeinated Capital and Initialized Capital. Additionally, Ali Rowghani, previously Pixar CFO and Twitter COO, will join the board of directors.

The company was founded in 2015 with the ethos that scientists who have followed non-traditional education and career paths should not be prevented from going after well-paying jobs when they have the right skills. TripleByte created a platform which uses coding quizzes and ML to match up would-be-employees with jobs that would be a good fit for their skillset.

"[My cofounders and I started] Triplebyte to solve the problem that every company now has to build their own software and they need to find engineers to do it," explained TripleByte CEO and co-founder Harj Taggar. "There's not enough engineers graduating from traditionally well-known colleges to meet this demand, but those are the only places companies know to look for talent. The solution is creating a new credential that companies can use to find engineers from any background."

Over the last year, its revenue grew three times (more than $1m monthly) while its headcount doubled from 20 to 40.

Taggar said that the new funding will be used to help the company expand beyond the Bay Area and into Los Angeles, New York, Seattle and Boston later this year.

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