It’s really looking like another rat race. Especially since there’s no central authority, every hiring manager has the potential to invent their own filter, and make it arbitrarily harder or easier based on supply and demand (and then the filter drifts away from the intended purposes).
I don't think I'm a bad engineer, but I'm certainly not the rock star you absolutely need for your team, but when it comes to this kind of “cleverness” tests, I'm really really good.
I've had the “Queen Killing Infidel Husbands" (with another name) in an interview last year and I aced it in a few minutes, and I didn't knew about "Pirate Coins", but when I read your comment HN said your comment was "35 minutes ago" and now it says "40 minutes" which means I googled the problem, figured out the solution and then found the correction online to see if I was right in less than 6 minutes, and so while I'm putting my son to bed!
It's really sad because there are many engineers much better at there job than me who will get rejected because of pointless tests like this…
I had to fight my way into google by doing every bit of prep and practice to solve stupid questions and code quicksort but when I joined, nothing I did in the 12 years I was there required any of that. And I wrote high performance programs that ran on millions of cores (I did know some folks who needed that skill, like the search engine developers, or the maps engine, or the core scheduling algorithms in borg). The entire time I was there I tried to get people to understand the questions they're asking are just not good indicators of programming, but it was repeatedtly pointed out, the goal is to minimize false-positive hires.
I do admire your ability to solve problems like that quickly, always wished I could.
Testing for geekyness and ability to solve tricky coding math problems, seems like a rational way to do that.
If companies were starving for talent because 'nobody could pass the test' - it would be another thing.
But they have to set the bar on something, somewhere.
I can't speak to AI/ML but I would imagine it might be hard to hire there, given the very deep and broad concepts, alongside grungy engineering.
I've rarely had such fascination and interest in a field that I would never actually want to work in.
Has humanity just scaled way too hard or something, because if we’re having an abundance of supply in difficult cutting edge fields to the point where they also have their own version of Leetcode, then what hope do average people have of getting any job in this world?
Or, is it at all possible that companies are disrespecting the candidate pool by being stingy and picky?
Maybe the truth is gray.
The absolute demand in number of people is small compared to popularity. It would not surprise me at all if many computer science master's programs had a majority of the students studying machine learning. I remember in undergrad we had to ration computer science classes due to too much demand from students. I think school had 3x majors over a couple year time period in CS.
The number of needed ML engineers is much smaller than total software engineers. When a lot of students decide ML is coolest we have imbalanced CS pool with too many wanting to do ML. Especially when for ML to work you normally need good data engineering, backend engineer, infra, and the actual ML is only a small subset of the service using ML.
At the same time supply of experienced ml engineers is still low due to recent growth of the field. Hiring 5+ years of professional experience ML engineers is more challenging. The main place were supply is excessive is for new graduates.
I think it's just a matter of proliferation of these types of programs, as well as a large supply of students.
Also, the average qualification of people working in ML is probably no longer a Ph.D, like it used to be. This is arguably because deep learning techniques require less involved math to understand, and are more focused on computational methods that work well.
So the field has probably saturated. When I got involved with ML for the first time (well, really, statistical signal processing) in the mid 2000s, the field was kind of dead, and very high qualified postdocs had tough time finding jobs.
I don't know for ML, but there are almost 12k Masters CS degrees awarded per year and 1.1k PhDs. If my university is any indication, then there's a good portion of those that are ML or doing some sort of ML in their research. But even if it was just 10%, that's a lot of people per year that are being added. This is just the US btw.
