8 ms·

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

by gzer0 2y ago

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 reality, however, is that these numbers are just absurd.

xandrius 2y ago
I think at that point it's a lottery anyway, pick 50 truly randomly and try those?
- teddyh 2y ago
  “We don’t want to hire unlucky people.”
thaumasiotes 2y ago
> With that kind of applicant pool, I’m honestly not sure what the best approach is You should see the number of people that apply to college.
- turdprincess 2y ago
  At least in the USA one major piece of the college application is a standardized test - a concept very similar to a Leetcode interview.
  - sotix 2y ago
    A standardized test is considerably more standardized than a leetcode interview though. Not to mention you take it once and then provide the scores to everyone. My process with taking the cpa exam a single time was a much better experience than random leetcode problems every time.
lordnacho 2y ago
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.
- dahart 2y ago
  That, to some degree, is guaranteed to happen naturally. But why not use a sieve approach and narrow the pool exponentially using a range of tools that goes from automatic and instantaneous at the start to manual and time-intensive interviews at the end? The potential upside for a company is relatively large - with a small amount of work they have the opportunity to get the approximately best 30 people out of 100k graduating students. From the company’s perspective, this isn’t a problem at all, it’s a massive windfall of an opportunity. They can afford to optimize it, and they’re motivated to. As a hiring manager or company founder, I’d love to have this “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.
  - lordnacho 2y ago
    The problem is noise. Say you want to take 10% at each round. You have to do this three times to get 100 people, and then another round to get the top 30. If you tighten the criterion, noise matters more, so you will drop your actual best candidates, since people need a high skill+noise. But if you try to keep the unlucky "good" candidates by having a wider band, you pay more. Also, if you are successful at finding the best candidates in a batch, noise matters even more the next round. I've been thinking about how to vizualize this. I have it in my mind and I'll try to describe it. You plot a bunch of points with S, N for each candidate. They are independent, so a scatter plot looks like a 2D gaussian. Lets say skill is vertical, and noise is horizontal. You want to find the 30 highest points, but you aren't allowed to just look on the scatter plot and select the top points. You have to draw a line S + N = c, and choose the person with the highest c. Basically sliding a ruler out that crosses the S and N axes as far away from the origin as possible. Observation: if N is high (wider distribution) compared to S, you just get the luckiest people. If S is high compared to N, you just get the most skillful.
axus 2y ago
Wow, 30 for all of Microsoft... they probably lose 30 people a month just from attrition!

by gzer0 2y ago

xandrius 2y ago

I think at that point it's a lottery anyway, pick 50 truly randomly and try those?

teddyh 2y ago
“We don’t want to hire unlucky people.”

thaumasiotes 2y ago

> With that kind of applicant pool, I’m honestly not sure what the best approach is You should see the number of people that apply to college.

turdprincess 2y ago
At least in the USA one major piece of the college application is a standardized test - a concept very similar to a Leetcode interview.
- sotix 2y ago
  A standardized test is considerably more standardized than a leetcode interview though. Not to mention you take it once and then provide the scores to everyone. My process with taking the cpa exam a single time was a much better experience than random leetcode problems every time.

lordnacho 2y ago

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.

dahart 2y ago
That, to some degree, is guaranteed to happen naturally. But why not use a sieve approach and narrow the pool exponentially using a range of tools that goes from automatic and instantaneous at the start to manual and time-intensive interviews at the end? The potential upside for a company is relatively large - with a small amount of work they have the opportunity to get the approximately best 30 people out of 100k graduating students. From the company’s perspective, this isn’t a problem at all, it’s a massive windfall of an opportunity. They can afford to optimize it, and they’re motivated to. As a hiring manager or company founder, I’d love to have this “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.
- lordnacho 2y ago
  The problem is noise. Say you want to take 10% at each round. You have to do this three times to get 100 people, and then another round to get the top 30. If you tighten the criterion, noise matters more, so you will drop your actual best candidates, since people need a high skill+noise. But if you try to keep the unlucky "good" candidates by having a wider band, you pay more. Also, if you are successful at finding the best candidates in a batch, noise matters even more the next round. I've been thinking about how to vizualize this. I have it in my mind and I'll try to describe it. You plot a bunch of points with S, N for each candidate. They are independent, so a scatter plot looks like a 2D gaussian. Lets say skill is vertical, and noise is horizontal. You want to find the 30 highest points, but you aren't allowed to just look on the scatter plot and select the top points. You have to draw a line S + N = c, and choose the person with the highest c. Basically sliding a ruler out that crosses the S and N axes as far away from the origin as possible. Observation: if N is high (wider distribution) compared to S, you just get the luckiest people. If S is high compared to N, you just get the most skillful.

axus 2y ago

Wow, 30 for all of Microsoft... they probably lose 30 people a month just from attrition!