Earlier quoted context omitted.
The core issue here is the sheer volume of applicants. Microsoft opened 30 new-grad software engineering positions. Care to guess how many applications they got within 24 hours? 1,000? 10,000? Nope. 100,000 applications. In under a single day. With that kind of applicant pool, I’m honestly not sure what the best approach is—even though, in a perfect world, your suggestion would be the more appropriate route. The real…
The problem is attempting to optimize a noisy process. Instinctively people think they need to look at all the applicants. Start interviewing people, and once you have enough people who pass your bar, you tell the rest tough luck. Secretary problem.
That said, your comment reminds me of a Monte Carlo algorithm I think I’ve heard about. There is a way to have some statistical confidence in getting the top K out of a sample of N without examining all N samples. I’m blanking on what it’s called, and I think it’s related to Reservoir Sampling. I don’t know if I read this or am making it up, but my instinct is that you can get to high levels of confidence after looking at sqrt(N) samples.