[jollybean]

But if there is an abundance of supply, the company has to use some kind of filter.

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.

[crate_barre]

There’s an abundance of supply of people with masters degrees in machine learning? How’s that possible? I thought this shit was supposed to be hard.

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.

[Mehdi2277]

I currently work as an ML engineer and have interviewed on both sides for some well known companies.

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.

[fault1]

> There’s an abundance of supply of people with masters degrees in machine learning? How’s that possible? I thought this shit was supposed to be hard.

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.

[godelski]

> There’s an abundance of supply of people with masters degrees in machine learning? How’s that possible?

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.

https://datausa.io/profile/cip/computer-science-110701