zlacker

AFAIK their promise of "we use machine learning to..." never panned out even remotely. All the processes ended up being mostly manual, with all the tradeoffs that entails.

With the money they raised, after spending so much on marketing, I assume they downsized, lost some talent, and pivoted mostly to a sales-driven recruiting business for their top clients.

>>tmp_an+(OP)
Yeah, the economies of scale that VCs are looking for weren't there.

And even if they stayed to their original model it would have been too easy for niche competitors to erode their margins. Think Triplebyte for Android developers only times 20 different programming areas.

>>tmp_an+(OP)
That was true of our process as it existed in 2017/2018 (and was a part of why that business was not viable). At this point, what we do is develop tests (backed by psychometric models). These are more accurate than human phone screens (and especially more accurate at finding people who have strong skills bad "bad" resumes)

>>ammon+Hd
> These are more accurate

I can't imagine the difficulty to accurately measure the success or failure of long-tail HR hiring processes like phone screens. The success or failure of a candidate post-hire has so many variables it must be very hard to attribute them to signals present in a screen. I imagine most of the data points are derived from signals found in successful candidates, and then trying to find them in an assessment or screen.

Its really hard, and I hope the negative tone of my comment does not suggest I don't respect the problemset and the people willing to throw themselves at it.