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The Machine Learning Job Market

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Re: The Machine Learning Job Market

#121

Post by benibela »

PartiallyTyped wrote: post #19
SCUSKU wrote: post #12

I was expecting something along the lines of how AI/ML was considered a sexy career path, but there are very few jobs available and high competition for those jobs. So, as a result, you end up with the only available jobs as data engineering/ML ops/backend that supports ML teams. I am happy for this author and their success, but they clearly are not representative of the majority of the people in the ML job market.

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

Re: The Machine Learning Job Market

#122

Post by cellis »

opensrcken wrote: post #107
greatpostman wrote: post #43

Earlier quoted context omitted.

600k is a senior engineers wage

No, it's not. If you get lucky with stock market movements, that might be your total annual comp , which is not your wage. I made close to what you're claiming at senior level, at the recent peak of stock market insanity, but I'd stick to the dictionary definition of "wage."

This all day. Sure if you joined FB in March 2020 your total comp today will be a multiple of that 700k, but FB is not giving out 600k packages today to e5s ( and e5 imo is staff+ at many lower tier companies ), more like 450k all in compensation.

Re: The Machine Learning Job Market

#123

Post by pbronez »

uoaei wrote: post #106
colonelxc wrote: post #73

Earlier quoted context omitted.

In general, yes, I think that would be okay. I think it would be a mistake to create separate sections for each level. Overall your achievements within a single company should not be organized chronologically, but by what you want to show off. This may still be mostly chronological as you take on more responsibility/leadership. Now, I don't object to adding a line like: "promoted twice from Software Engineer 2 to Sta…

But it undeniably misleads the reader into thinking this person has held senior responsibilities since 2016, which is outright false, and may instill in the reader more confidence than is due.

As a hiring manager, I don't assume that. When there's a single title over a long range of time, I assume it's a terminal title. I look to the details for the position to see what kinds of work they've done. In the interview, I'll dig into trajectory and experience at various levels.

Re: The Machine Learning Job Market

#124

Post by amelius »

angarg12 wrote: post #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 possi…

Machine Learning expert is the new Web Developer, so expect the former title to become diluted very quickly.

Re: The Machine Learning Job Market

#125

Post by cellis »

vmception wrote: post #23
claytonjy wrote: post #15

Earlier quoted context omitted.

This doesn't sound quite right to me; Coinbase pays well, but looking at levels.fyi it's not FAANG money; is there someone else you have in mind? Would love to hear more about the startups; I tend to turn down such opportunities far before we talk non-cash comp.

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…

It’s important to keep in mind that working at the next Solana Labs, Alameda Research etc is roughly equivalent in probability as getting drafted in the NBA. That is to say, there aren’t a lot of cases that happen.

Re: The Machine Learning Job Market

#126

Post by warner25 »

Can someone with only 6 years of experience make credible predictions about things 20 years in the future?

I'm around 15 years of experience, and my appreciation for my own lack of knowledge and ability to make predictions still grows with every year.

Re: The Machine Learning Job Market

#127

Post by atorodius »

samhw wrote: post #84
going_ham wrote: post #76

Earlier quoted context omitted.

Yeah, and the way the author presents themselves speaks volume too. There is a real pride in this essay. You can see how the author casually drops big names and insights like it is a fact. Why does valley culture makes it seem like everything is possible and anything innovative can happen soon? The innovation in AI really seems like it is being made on a thin line of engineering and compute. It doesn't happen overnig…

> The innovation in AI really seems like it is being made on a thin line of engineering and compute. This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And ev…

> ‚make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems

I take it you have not seen the recent Dall-E 2 results? Clearly that model is not just working on a narrow space.

See https://openai.com/dall-e-2/ and the many awe-inspiring examples on Twitter

Re: The Machine Learning Job Market

#128

Post by hervature »

cletus wrote: post #47
hervature wrote: post #6

Earlier 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…

These outliers are rare but they do exist. I knew someone at Google who was hired as L3 straight out of college (as all non-PhDs are) and got promoted once a year to L6 (Staff) so 3 years. He got promoted to L7 2 years after that. It's a rare combination of talent and the right circumstances but it does happen.

I tried to hint at this by using quotes, I don't doubt that L6 is possible. But, please elucidate, are there L6s at Google making "low 7 figures"? From levels.fyi, there are no such reports. The average is about half and matches what I know from other companies. Those that are approaching 7 figures have at least a decade of experience. Anyway, the pay he describes is much closer to L8.

Re: The Machine Learning Job Market

#129

Post by visarga »

samhw wrote: post #84
going_ham wrote: post #76

Earlier quoted context omitted.

Yeah, and the way the author presents themselves speaks volume too. There is a real pride in this essay. You can see how the author casually drops big names and insights like it is a fact. Why does valley culture makes it seem like everything is possible and anything innovative can happen soon? The innovation in AI really seems like it is being made on a thin line of engineering and compute. It doesn't happen overnig…

> The innovation in AI really seems like it is being made on a thin line of engineering and compute. This perfectly echoes my own thoughts. The advances being trumpeted in AI are functions of hardware advances that allow us to have massively overparameterised models, models which essentially 'make the map the size of the territory'[0], which is why they only succeed at a narrow class of interpolation problems. And ev…

I disagree, there are plenty of amazing advancements in the last 2 years you can't write off like that (especially Instruct GPT-3 and Dall-e 2). For example I have worked on a ML project in document information extraction for 4 years, and recently tried GPT-3 - it solved the task zero shot.

Re: The Machine Learning Job Market

#130

Post by krat0sprakhar »

titanomachy wrote: post #110
hervature wrote: post #6

Earlier 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…

FAANG staff in 5-6 years out of school is not impossible. I know a couple. They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence. If I had to guess, I'd say 1 in 30 from the population of Google-level engineers.

> They are significant outliers in terms of focus, dedication (i.e. hours worked), and raw intelligence.

As someone who has worked at FAANG for 5 years right out of school, getting to staff is less about raw intelligence and more about being lucky with working on projects that did not get canned and finding supportive managers. My friends much smarter than me have not had a good growth purely because they were unlucky with initial team assignment and PA / reorgs cancelling their projects.

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