Rejecting Good Engineers?
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When I was at Google, the general feeling was that we rejected a lot of good candidates. I don’t say unnecessarily reject though, because it is in fact necessary to reject them. As anyone who studied statistics or machine learning (which is many or even most engineers and managers) knows, there are two types of errors: false positive and false negative. That is, hiring someone unqualified and not hiring someone qualified. You can arbitrarily decrease one of these at the cost of the other, but it’s very hard to keep both low.
Prestigious companies almost always heavily focus on not hiring unqualified people. This is because they get a lot of qualified candidates, and the cost of a mistake in not hiring is largely the loss of interviewers’ time, since another candidate could be found. The cost of a mistake in hiring is tremendous. It costs a lot of money, time, and team morale to get rid of someone unsuitable.
The other reason for this is objectivity. You really want to hire people who can satisfy as objective criteria as you can make it. One obvious (and minor) reason is that this way you don’t get sued for discrimination, and if you do, you have a lot of evidence to defend yourself. The major reason is that there is a tendency by people to be lenient when there’s work to be done, and you think that hiring a warm body would do. If that someone can’t pass some objective (even somewhat arbitrary) criterion, you can’t compromise your standards, or if you do, there’s clear evidence that you did so. So senior executives can make a rule to make hiring managers accountable if they substitute personal judgement for objective criteria. At Google, of course, hiring was centralized and so the decision was made by a large number of committees which were trained not to pay much attention to personal opinions of interviewers at all, but mostly to the submitted evidence of the candidate’s performance, in the form of records of their answers. Candidate’s experience as expressed on the resume, but not demonstrated in the interviews was often irrelevant for the hiring decision (which is why interviews are supposed to test for more than just algorithms) because people lie and exaggerate. If the person was considered good enough, then the experience comes into play to level them, find them a team, and so on.
In other words, rejecting a qualified candidate is a necessary cost of doing business for many companies. But a smaller company might take a chance, either because the decision makers there don’t have to justify themselves to anyone, or because they know that perfect candidates are hard to find and they need someone good enough now.
