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Funny enough, my experience is NLP and DeepRL.
Your best chance might be to apply at small companies. They often have data and want to take advantage of it but haven't so far. Of course they're not doing any research or are Google, Amazon, etc. but hey.
- 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.
"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).
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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.
I don't think that is unusual to list the latest level on a resume. I'm certainly not going to dedicate space on a resume to list time ranges for every promotion.
It's different if the level isn't represented in the title - if they went from one band to another but the title was the same, I don't see a problem with putting down something like "Software Engineer, 2016-present" without wasting space on each promotion.
"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).
> 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.
(Not saying anyone "deserves" that or that's how it should be, but that's just how it is here in the valley.)
"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).
"You can only be level X with compensation Y after Z YOE" is one of the greatest infohazards in tech.
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.
- 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…
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?
The other side is deploying it efficiently, and that becomes a more routine software engineering problem. Fundamentally you have some code that you want to run as fast as possible on the cheapest hardware you can feasibly use. Large companies like Google have the luxury of splitting this out into several distinct roles - from pure researchers (people publishing papers), to people who train models for business purposes (eg the Google Lens, computational photography, Translate), to people who optimise the ML library code underneath, to people who build out the end user application with the ML model as a black box service.
Most of those people don't need to know much ML, but the exposure can help you transition into a more ML focused role.