Hey thanks for the reply. I don’t really understand this answer to be honest. How do you compare fairly between two or more candidates applying for the same position with this method? If you’re judging all applicants on the “would you want to work with them scale” what are the common parts of the scale across applicants as it applies to this technical project? How do you ensure the judgement of wanting to work with someone is fair within a pool of applicants for a position?

Hmm, I think we're just looking at it differently. Why does fair mean "everyone is graded exactly the same, whether they're good at this certain type of problem or not"? Couldn't it be argued that the most fair way to judge people is to treat everyone individually?

Given all of the vagaries in the hiring process, do you believe that e.g., algo approaches are actually fair in the way that you intend?

Then your method doesn't scale.

What makes you believe that? Do you believe that the e.g., algo approaches create a strictly rank orderable list of candidates which actually correlates to their on-the-job impact? Also, why do you think such a thing matters more at scale than when a team/company is small?

From the op - "We never choose between two candidate, rather each candidate is judged on if we want to work with them or not. It’s an advantage we have as a small company! "

That doesn't answer my question about why you claim it doesn't scale.

When the company gets big / popular enough, they have to choose between 2 candidates for the same position. All these policies will then go away.

I think it does. But even if not, that's fine! Until we have to scale it, it's an unfair advantage we have.

Like YC said, "Do things that don't scale."