all the CS postdocs I know see a) pretty happy b) have a really easy back up of a high paying industry gig that they can pivot to because of currently insanely inflated demand for academics to make deep learning on the blockchain on the cloud on embedded devices doing quantum backprop
I am primarily interested in the experience of ML post-docs that forgo going into industry. Can you elaborate a bit more about their experience? Salaries look a bit lower for post docs vs industry work. Some I've seen start in the mid-40k to 70k range. Could be different based on geography.
are you already a grad student or are you considering it?
in my department [we are an outlier probably] post doc wages are actually pretty comparable to industry i.e. >= 100k. 40k to 70k seems low to me.
I would expect the range to be more like 60-100k.
I haven't been on the real job market yet-but the key to getting a TT or postdoc position seems to be a) collaborators who want to hire you b) people who have heard you give a talk in person and are familiar with your research.
I am considering going into a CS PhD focusing in ML. The mid 40k-70k range was from quick google search I did for CS post docs in ML in lower cost of living areas where the cost of living is much lower than on the West Coast. I am trying to look at career prospects and weigh whether it makes sense to stay in academia or jump to industry (after I complete a PhD). If wages are closer to 60k-100k for post docs, then I may consider staying in academia for some time after completing a PhD depending on whether my career interest shift.
Well I would be happy to provide some context.
I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far.
The other thing to note in ML is that it seems like a few people go to industry research labs for a few years i.e MSR/FAiR/google brain and then come back to the academy since there are industry roles that involve research and publication. for instance moritz hardt.
my personal plan for the first 3 years of grad school is to work really hard and try to keep both academia and industry open and after year 3 evaluate the number of publications I have and my current skill set to see if I can make it in the academy or shift more towards industry.
I think the biggest factor I would comment on is look very closely about what jobs the graduated students from the department you matriculate at AND more importantly the professor you want to work with go on to do post Phd. There are a lot of naysayers in this thread about the risks of an academic career and I share those concerns but I felt a lot more comfortable taking the plunge after I looked at the career record of the graduated students of my advisor. They were all either tenure track or had good industry positions.
edit: if your advisor has collaborators in industry groups I think it is pretty straight forward to get an industry gig.
When evaluating the warnings from naysayers you have to keep in mind that CS is quite an outlier as far as backup career prospects go. I made it all the way to CS postdoc and every step of the way I had to keep swatting away industry recruiters waving wads of cash at me. I finally made the leap for other reasons but it was effortless. I think this is absolutely not how it works in other disciplines.
One exception I can think of is what I call "closet programmers," which are folks that work in various areas which rely on software such as experimental physics, astronomy, molecular biology. and end up mostly doing programming because they love it. We have a bunch of engineers like that and they are all excellent :-)
Thanks for the context. That sounds like a good plan to me regarding post-doc locations. I am also interested in theory side of ML. What areas of mathematics should one learn really well that apply to the theory side? What blogs, papers, books would you point one to to learn the theory side more? To your knowledge are their applications of abstract algebra to ML? If so, what areas of algebra apply & what problems do they solve?
all the CS postdocs I know see a) pretty happy b) have a really easy back up of a high paying industry gig that they can pivot to because of currently insanely inflated demand for academics to make deep learning on the blockchain on the cloud on embedded devices doing quantum backprop
-these are ML/theory postdocs