99posse
99posse t1_j2xatk7 wrote
Reply to comment by Farconion in [D] life advice to relatively late bloomer ML theory researcher. by notyourregularnerd
Google in Zurich or London pays more than in Mountain View (this is the one I know, not sure if Meta does the same)
99posse t1_j2smjnr wrote
Reply to comment by crispin1 in [D] life advice to relatively late bloomer ML theory researcher. by notyourregularnerd
Sure, that's why you can spend the rest of your life working on enumerative combinatorics (which, BTW, I just love). Just don't fool yourself thinking you are making the world a better place with a bunch of papers.
I am not disputing the morality of this, just stating what I see/know having worked in both academia and industry.
99posse t1_j2sm1tr wrote
Reply to comment by klop2031 in [D] life advice to relatively late bloomer ML theory researcher. by notyourregularnerd
Not just with that, but take a look at levels.fyi and check how much a 1-level advantage at Google or Meta gives you as you progress (the 250K/year I mentioned is just comparing MS + 4Yrs to PhD)
99posse t1_j2pyap8 wrote
> Any suggestions on pros and cons of ML research in academia vs a ML industry job of a masters grad would be really helpful!
I am very opinionated on this, so you may want to weigh this accordingly.
PhD: I wish I could get my time back. I would be comfortably retired by now. 4 or 5 years industry experience working for a FAANG gives you a >USD250K/year advantage in the US (plus 4/5 years of being in a highly paid position). The only way you can compensate for this is if you get a PhD is a super relevant area that happens to be exactly what they are looking for (rare nowadays).
Theoretical ML: It's BS, literally. Pretty much everything being published is circlejerk by a small number of people playing with toy models and using plenty of math to hide the fact that they do not know what's going on. One interesting, recent observation is that as you scale models up, the specifics of the architecture no longer matter much and pretty much anything reasonable will work well enough. Which brings me to
Academia vs. industry: There are a few industries that do applied research at a scale that makes a difference. This is because (1) they have access to unlimited training data (2) they have access to nearly unlimited resources (3) they have urgent, real problems to solve (usually, advertising :-) ). What happens in academia (when they not partner with one of these companies) is laughable
99posse t1_iuv3z54 wrote
For me the main advantage was that reviewing and organizing a conference forced me to read quite a few papers back to back, very carefully, verify results and look at the references. When I read for work, I only care about what I need and skip all the boring stuff (a lot, usually).
Talking in the past as I decided to leave the organization of the conference to younger people, stopped all reviewing, and abandoned the IEEE mafia.
99posse t1_j2ynomd wrote
Reply to comment by Farconion in [D] life advice to relatively late bloomer ML theory researcher. by notyourregularnerd
I see (and agree with) your point (I am a European citizen working in the US) but ML work at that scale is not that common. If you want to stay local, academia and theoretical work are your best shots (with all the salary implications of the case).