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The Machine Learning Job Market

evjang.com

61–70 of 276 posts

Re: The Machine Learning Job Market

#61

Earlier quoted context omitted.

Levels fyi shows its FAANG money at all levels I have it listed alongside Google and Amazon and Facebook right now, what did you compare that seemed different? (They’re not reporting consistent 7 figures for any of them) did you see something more granular? Outside of publicly traded crypto companies you need to talk to a third party recruiter in that space Solana Labs, for example, one of many, was paying engineers…

so you are saying there's a chance these solana engineers are sitting on a billion in crypto?

probably, that's what tends to happen when you literally make money

Re: The Machine Learning Job Market

#62

> 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…

Not every problem is one of scale. And with the advent of serverless, problems relating to scalability will be largely abstracted from 99% of developers in the future and more of a niche knowledge domain. Just as the inner workings of the OS are largely not well understood by most developers

Obviously the principles and theory behind scalability is still important for properly structuring your app, but there won't be many novel problems to solve, and increasingly obvious architecture choices as time goes on

Re: The Machine Learning Job Market

#63
I appreciate the post in the sense that it is an insightful perspective that he didn’t have to share. If you are elite it is difficult to talk about your options, pay or way of thinking without it coming across as nothing but hubris to the rest of us.

Re: The Machine Learning Job Market

#64

Earlier quoted context omitted.

On the other hand, there’s a lot of real problems that real people actually deal with that just need a logistic regression to save million bucks here and there. I like that space more.

This space isn't sexy to write about, there's no fame and glory in it.

[deleted]

Re: The Machine Learning Job Market

#65

> 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…

This has become increasingly important to me too. I am employed by a small (2-4 engineers at any time) company and I'm often disappointed because we're just so far behind in manpower & technical expertise that we have to dramatically reduce the scope of any problem we want to tackle.

On the other hand, I also worry about getting sucked into the bureaucracy of FAANG sized companies & not having any accountability or agency over what I work on. (I realize this is a sweeping generalization of FAANG, but some of my peers have had this experience even a few years into their jobs)

Re: The Machine Learning Job Market

#66
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.

On the plus side, the article accidentally produces the most useful definition I've seen of AGI. If you just define AGI as the the union and convergence of all hard problems, then you can just say "I'm working on AGI" to let people know you're doing the smartest, hardest thing, without sweating any details.

Re: The Machine Learning Job Market

#67

Earlier quoted context omitted.

Levels fyi shows its FAANG money at all levels I have it listed alongside Google and Amazon and Facebook right now, what did you compare that seemed different? (They’re not reporting consistent 7 figures for any of them) did you see something more granular? Outside of publicly traded crypto companies you need to talk to a third party recruiter in that space Solana Labs, for example, one of many, was paying engineers…

so you are saying there's a chance these solana engineers are sitting on a billion in crypto?

collectively? yeah, sure. this is absolutely probable in any organization of that valuation/marketcap, the most interesting thing here is just how fast crypto organizations can accrue and extract value.

tech sector is fast, crypto subsector is like an order of magnitude faster. its similar to tech employment in the 90s where there was fast vesting (mostly due to quick exits), liquidity at super low valuations that then rose extremely quickly and attractive compensation. the main difference now is that the valuations are much much higher. you can tap in sometimes/often at very low valuations - of the token - and also ride them up all the way to billions valuation very quickly. if they solve a market need (within the crypto space) then they attract value very quickly, sometimes that market need can just be the entertainment coming from hype, but most times its bandwidth since there is not enough blockspace to go around, periodically.

Re: The Machine Learning Job Market

#68
post #41
post #22

Interesting that Tesla gets its own row in the pro/con table while the faangs get lumped together.

If Musk is to be believed their Teslabot is going to be in a class by itself.

If Musk is to be believed, Flint would have clean water.

Re: The Machine Learning Job Market

#69
Author is going to Halodi Robotics.

I always thought that human shaped robots are a terrible form factor. Why limit yourself to the awkward design that 3.77 billion years of evolution accidentally landed on?

Re: The Machine Learning Job Market

#70
I don't want to derail the conversation, but OPs career path really stood out to me.

He graduated in 2016, worked at Google in Bay Area, and now is joining a startup at a VP level.

I graduated in 2008, obtained a PhD in 2014 in a no name EU university, worked in odd companies for a while and joined FAANG 4 years ago as a mid level developer, where I am still ATM.

Looking at this disparity I wonder what could be possible explanations:

* OP is a beast and has grown very quickly in a short time.

* I'm particularly inept and I'm growing very slowly.

* Working in the right conditions (e.g. Bay Area, Big Tech, right team) can greatly accelerate your growth.

* Startups have a big title inflation.

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