Good question! We’re lucky we don’t need to worry about this for engineers. 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! We can rate people based on whatever criteria gives them the best chance, and we can find a ton of great candidates that maybe wouldn’t make it through a traditional hiring interview at a larger company.
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?
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! "
It’s an advantage we have as a small company! We can rate people based on whatever criteria gives them the best chance, and we can find a ton of great candidates that maybe wouldn’t make it through a traditional hiring interview at a larger company.