If you're interested in AGI take a look at my effort currently in development. It focuses on natural language understanding: https://lxagi.com
The Machine Learning Job Market
241–250 of 276 posts
Re: The Machine Learning Job Market
#242The post is not only too braggadocious for my taste, but some of the figures quoted are highly unlikely. I would personally not work for someone with this kind of ego, but there are many such people in positions of power. This article is representative of an attitude I'm seeing around the tech industry, and if this is indeed the level of "confidence" in the Bay, I don't think that's a good sign.
Which figures do you think are unlikely?
Re: The Machine Learning Job Market
#243Earlier quoted context omitted.
Eric Jang is top ML talent, these numbers are accurate. I work in ML and have followed his work for years
If he's actual top talent as opposed to a poseur who's good at self-promotion, he should stay in academia for his own sake because he'll be crushed in the corporate world. Actual high IQ people get clobbered in corporate, while OKR-ing charlatans climb the ranks effortlessly... yes, even at FAANGs.
Have you seen his previous employment though?
He's exposed to enough corporate work. https://www.linkedin.com/in/evjang/
If he doesn't like this place, he can just make another post like this and I am sure ML startup CEO and ML division heads will be flooding his inbox.
Re: The Machine Learning Job Market
#244Earlier quoted context omitted.
Especially if you have Python experience, then yes the opportunity definitely exists. For example when I hire MLEs (which I am doing now if anyone wants to apply - supportlogic.io) I am willing to look at people who are solid Python/backend engineers and who have been "ML adjacent" or who we believe could learn the ropes of ML enough to contribute. The stronger an engineer, the more flexibility we have in ML knowledg…
Interesting. Honestly to me Python and backend engineer are effectively orthogonal skillsets though. I would expect any decent programmer to pick up Python in about a week... (slight exaggeration but you get the point).
Pick up to what point?
Re: The Machine Learning Job Market
#245Earlier quoted context omitted.
People are conflating SWE (or "research scientist" in name) bands with research scientist bands at labs like Brain. This guy is an outlier. "You can only be level X with compensation Y after Z YOE" is one of the greatest infohazards in tech.
Fwiw, the pay bands at Brain are the same as the pay bands everywhere else, with the sole exception of stock grants in initial offers.
That probably matter a lot.
Re: The Machine Learning Job Market
#246Earlier quoted context omitted.
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.
It's clear on his LinkedIn. It's not unusual to to list the latest on a resume.
Re: The Machine Learning Job Market
#247> Low 7 figures compensation (staff level) Odd choice of level, since the author worked at one of these companies and was not at that level, certainly did not make 7 figures.
They spent 2 years at the senior level at one FAANG. Why would they switch to another for anything less than the staff (senior + 1) level? (Not saying anyone "deserves" that or that's how it should be, but that's just how it is here in the valley.)
and not just generic senior SWE level.
Re: The Machine Learning Job Market
#248Earlier quoted context omitted.
In my view, AGI is farther away than fusion. We know how to do fusion. We know the physics behind it. We haven't yet figured out how to build profitable fusion plants, and we probably won't for a long time, if for no other reason than improvements in fission--modern fission plants are the best . When it comes to AGI, we have no clue. It's a constantly moving target, because our conceptions of intelligence evolve. Mos…
I disagree with part of this. Nature has proven that general intelligence can be achieved in a compact, energy-efficient form: humans. Has nature ever proven that nuclear fusion can be sustained at human scale? I would bet that agi comes first.
I think they're both sort of in the same place.
We're fairly certain it can be done given unbounded resources, we have some idea of the principles involved, but then there's a rather significant element of "draw the rest of the fucking owl" between where we are and where we imagine we could go.
Re: The Machine Learning Job Market
#249Earlier quoted context omitted.
I think I have a decent CV, with quite a bit of experience for a master's student. I have been searching for a job in MLE, for a bit now with very little to show for it, as I am either getting no responses, or responses claiming that they are looking for more experienced people, and particularly those that had experience with a particular stack. In all honesty, after 6 years of studying, with 4 of those years studyin…
That is why I am doing a Post Doc
May I ask what you're working on?
Re: The Machine Learning Job Market
#250> 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…
Well, I'd say they won't fully be abstracted away because devs will still have to pay for scalable systems (hw + sw) with dollars.
Granted, they won't have to think about designing a solution, but they still won't have the computational power which can be afforded by larger companies (and cloud computing is ridiculously expensive), unless they have a lot of money .