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The Machine Learning Job Market

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Re: The Machine Learning Job Market

#2
- This really isn't representative of the ML job market because the author is such an outlier.

- The fact that it isn't representative is what makes the article an interesting read.

- The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.

Re: The Machine Learning Job Market

#3
post #2

- This really isn't representative of the ML job market because the author is such an outlier. - The fact that it isn't representative is what makes the article an interesting read. - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.

> - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.

I see they're going the cold fusion route.

Re: The Machine Learning Job Market

#6
post #2

- This really isn't representative of the ML job market because the author is such an outlier. - The fact that it isn't representative is what makes the article an interesting read. - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.

As someone who is in a somewhat similar position as the author (looking for senior ML roles), I found this part enjoyable:

> I’m not like one of those kids that gets into all the Ivy League schools at once and gets to pick whatever they want.

Followed by "FAANG + similar" and a deluge of options. Also, I feel like their message is pretty liberal with using future projections and implying it to be the present. For instance, the author has 6 years of experience with 2 at the senior level. This is pretty far from "staff level" (at 1M+ compensation, I think this is L8) which they imply is/was an option at a FAANG company. I don't doubt that in 5 years they would be at that level, but they almost certainly did not get offered a "staff" position at a FAANG.

Re: The Machine Learning Job Market

#7
You forgot to write that crypto startups compete with publicly traded FAANG on compensation on both cash and non-cash compensation, and there is no liquidity issue whatsoever on the non-cash they pay you with. Vesting schedules are more competitive than FAANG.

And the publicly traded crypto companies compete with FAANG on compensation too.

Non-crypto startups are the only ones sitting in the doldrums left out to dry right now.

Re: The Machine Learning Job Market

#8
post #2

- This really isn't representative of the ML job market because the author is such an outlier. - The fact that it isn't representative is what makes the article an interesting read. - The fact that they claim to have a plan for solving AGI in 20 years really detracts from their credibility.

As someone who is in a somewhat similar position as the author (looking for senior ML roles), I found this part enjoyable: > I’m not like one of those kids that gets into all the Ivy League schools at once and gets to pick whatever they want. Followed by "FAANG + similar" and a deluge of options. Also, I feel like their message is pretty liberal with using future projections and implying it to be the present. For ins…

It's ambitious bordering on delusional. They're also doing the dirty trick of putting "2016 - 2022 Senior Research Scientist at Robotics at Google" on their resume even though they've been in the senior position only since 2020. Like, dude, you're doing great, your resume doesn't need any more artificial pumping up. Or I guess it does if you're aiming for those positions that are kind of out of reach.

Re: The Machine Learning Job Market

#9
post #4

A lot of opinions and unverifiable statements (this and this company is X years ahead of everyone), and the whole piece is essentially about one person's job market. Skip

I’m interested in understanding the ML job market for traditional software developers.

Does the opportunity exist to transition into any particular ML roles then grow from there?

Re: The Machine Learning Job Market

#10
> The most important deciding factor for me was whether the company has some kind of technological edge years ahead of its competitors. A friend on Google’s logging team tells me he’s not interested in smaller companies because they are so technologically far behind Google’s planetary-scale infra that they haven’t even begun to fathom the problems that Google is solving now, much less finish solving the problems that Google already worked on a decade ago.

^ this is increasingly a choice in your career -- 'where can you go to solve big problems', and 'big problems are increasingly complex'

scale is real, and tools matter. you can spend your whole project burn at the wrong company building something that you could buy somewhere else, or which already exists at a competitor

slight grain of salt here is that G's logging system, from my perspective as a gcp user, is slow as balls and the UX is the incarnation of scroll jank. and also this (very good) article led to the outcome of the author building soft hands happy-ending robots

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