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The Machine Learning Job Market

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Re: The Machine Learning Job Market

#91

Post by twomoonsbysurf »

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…

I know a former Amazon Engineer. After working at Amazon as a mid level engineer, co-founded his own startup in Mexico, as CTO.

It's a startup... titles in a 50 people organization don't compare to 50,000 people organization titles.

I'm sure you can go and be a VP at a startup too, if that's what you want to do. Just go and network at Incubator, Investor, & Entrepreneur events/meetups/organizations, and come up with an idea & customers, then execute and try to get customers on board... rinse and repeat.

Re: The Machine Learning Job Market

#92

Post by axg11 »

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…

Don't despair. I work for a FAANG and have previously worked at startups. Title inflation at startups is a huge factor. In fact, titles are not equivalent between any two companies. I have seen startup CTOs (even series A) transition to senior engineer IC roles at a FAANG.

As you identified, location is the next big factor. If you are still in Europe, my advice is to leave or to start your own company there. If you are working for primarily US based companies in Europe there will always be a limit to the level of exposure you get to leadership and to how fast you can rise up the hierarchy.

Finally, don't discount Eric's profile. Through some combination of his public profile and professional work, he's established a reputation and following. That is just as important as any hard engineering work in securing a senior/leadership role.

Re: The Machine Learning Job Market

#93

Post by queuebert »

"Product impact is even slower than robotics due to regulatory capture by hospitals and insurance companies."

The author apparently does not understand regulatory capture and is throwing around catch phrases to sound smart. Regulatory capture would imply that healthcare encounters less regulation than it should due to influence over the relevant government agencies. This should increase product impact and reduce time to market, the opposite of what he suggests.

Re: The Machine Learning Job Market

#94

Post by axg11 »

oneoff786 wrote: post #17
awinter-py wrote: post #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…

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 describes 95% of machine learning at FAANG+, unfortunately nobody likes to talk about. Context: I work at a FAANG.

Re: The Machine Learning Job Market

#95

Post by axg11 »

queuebert wrote: post #93

"Product impact is even slower than robotics due to regulatory capture by hospitals and insurance companies." The author apparently does not understand regulatory capture and is throwing around catch phrases to sound smart. Regulatory capture would imply that healthcare encounters less regulation than it should due to influence over the relevant government agencies. This should increase product impact and reduce time…

Doesn't the author's sentence mean: hospitals and insurance companies have coopted regulators for the benefits of their own businesses, at the detriment of medical device companies (developing AI)?

I think the author's point still stands.

Re: The Machine Learning Job Market

#96

Post by axg11 »

1024core wrote: post #26

"FAANG+similar : Low 7 figures compensation (staff level), technological lead on compute (~10 yr)" I don't know where OP is getting these figures from, but I doubt that FAANGs offer 7-figure comps to Staff-level people. It's probably more in the higher 6-figure level (400K - 600K).

OP is probably in the best situation to judge this since they likely had competing offers or at the very least know peers with competing offers at FAANG+ staff level.

Re: The Machine Learning Job Market

#97

Post by queuebert »

jstx1 wrote: 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 problem with AGI is that it is people like this who are developing it.

Re: The Machine Learning Job Market

#98

Post by time_to_smile »

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…

Tech has a bad habit of conflating comp/prestige with skill. I have no doubt the OP is quite good at what they do, but you not being where OP is does not therefore imply you don't have skill.

Unfortunately the tech world is not really a meritocracy.

When I look at my own circle of technical people the most incredible ones from a pure technical ability are divided between working at FAANG making 500k+ and working a relatively unknown companies making ~200K or less. One of the most mindbendingly brilliant people I know is working in relative obscurity, known very well only among other people that are top in the field, but their resume looks very ordinary compared to their behind the scenes contributions to major projects.

Managing a career in tech is largely independent from technical skills and abilities. I have met a shocking number of people making lots of money at prestigious institutions that are "meh" as far as technical ability goes (of course there's some great ones as well), and have met plenty of brilliant people working relative obscurity.

The success is largely a function of both background (Brown does beat a "no name EU university") and personal desire to have a prestigious career. There is a lot of self promotion going on in this piece, in fact the OP has already convinced you that they might be just a wildly better person than you. If they can convince you they are this amazing, then they also can convince the leadership team at a start up. But do recognize that their skill demonstrated so far is only in convincing you of this.

Re: The Machine Learning Job Market

#100

Post by antupis »

minimaxir wrote: post #90

As a relatively unremarkable data scientist/machine learning engineer of about 5 years, I've been keeping an eye on DS/ML positions as they tend to give a sense on what is important to companies in that space, although I'm not actively looking for a new role. More and more positions seem to require Ph.D. credentials even for non-senior roles, even though modern DS/ML tooling doesn't require it. If I ever left my job…

DE/ML ops/ Software engineer(data) is many ways new DS. Lots of greenfield projects and less competition.
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