15 ms·

A.I. researchers are making more than $1M, even at a nonprofit

173 points by alphagrep12345 7y ago

alphagrep12345 7y ago
How replicable is this thing? Looking from a monetary perspective, is it worth getting a Ph.D. in AI? Assuming you like both academic research and working in the industry, if you were to make a choice whether to do a Ph.D. or not, how would you make it?
- high_derivative 7y ago
  No, it's not. Im an AI researcher at a big tech company. It would have been far wiser financially to start as a software engineer after my Bachelor's. Also, having a few NEURIPS or ICML papers is not a hiring guarantee any more. It's decidedly not 2015-17 any longer. In particular, I feel bad for the people who started their PhDs with $$$ in their eyes around that time. Don't get me wrong, they will all be employable, but the field moves so fast, I have my doubts there will be cushy FAANG jobs for everyone capable of playing around with network architectures in a few years. It's a terrible idea to start one based on the hype few years ago.
  - alphagrep12345 7y ago
    I have a couple of questions - - Would you say that you'd have got an AI researcher position without a Ph.D.? - Also, why is NEURIPS or ICML papers is not a hiring guarantee? I thought they're highly sought after.
    - joshvm 7y ago
      > Would you say that you'd have got an AI researcher position without a Ph.D.? It's difficult to get any true researcher position without a PhD. It doesn't mean that PhD has to be in AI. Research involves a lot of reading and writing papers, which a PhD is supposedly training you how to do. That said most places will say "equivalent practical experience" and it's entirely possible to be competent in AI/ML without a PhD. I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. The interesting jobs (to me) are where domains cross, and it's also (hint hint) much easier to get a job doing AI for X than it is doing "fundamental AI". In any case, you're often doing stuff that nobody has done before anyway, but you don't need to spend your life hunting for the new ResNet. > Also, why is NEURIPS or ICML papers is not a hiring guarantee? What the OP probably might be implying is that everyone has a publication in NeurIPS nowadays. I think it goes deeper than that though, publishing in machine learning is broken. Having 10k people at one conference is not an efficient way to distribute research. You have to submit a full paper in November for a conference next Summer - pretty much only computer science does this madness. What's interesting is how unique this attitude is. In astronomy, for example, conferences are a fun place to catch up with folks in your niche. There might be a few hundred people and probably it'll be single-track. We publish whatever journal is the most relevant and they're generally all considered equivalent. Nobody cares if you publish in ApJ vs A&A vs MNRAS, if your research is good. There are also concerns that the quality of these venues is decreasing because the pressure to publish in them is so high. See https://arxiv.org/abs/1807.03341 https://arxiv.org/abs/1807.03341
      - neuralsp 7y ago
        >I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. Do you think it is possible to that without any background in anything? I mean could someone apply black box frameworks without understanding them. How would they be caught?
        joshvm 7y ago
        > Do you think it is possible to that without any background in anything? To do machine learning research? Or work in some random domain? > I mean could someone apply black box frameworks without understanding them. How would they be caught? Machine learning is rapidly becoming commoditised, but lots of people still don't understand just how much effort it is to get a good dataset and to prep Domain experts scoff at machine learning people who are trying to solve Big Problems using unrepresentative toy datasets, but also tend to have much higher expectations of what ML can do. Machine learning people scoff at domain experts for using outdated techniques and bad data science, but then propose ridiculous solutions that would never work in the real world (e.g. use our model, it takes a week on 8xV100s to train and you can only run it on a computer the size of a bus). There are also a lot of people (and companies) touting machine learning as a solution to problems that don't exist. Overfitting models is probably the most rampant crime that researchers commit.
        neuralsp 7y ago
        My question is whether someone could fake it and not be caught/fired. (So yes, I meant: "Or work in some random domain?") From the second half of your comment it seems that the answer is yes? Maybe a comparison would help: someone pretending to be an experienced iOS/Android developer without any qualifications or ability would quickly be caught. Since they couldn't produce any working app or use a compiler, and anyone can judge an app for themselves. You can't really just make it up out of whole cloth, people judge the results. You would have to start actually doing that, and if you couldn't or didn't want to, then unless you outsourced your own job or something the jig would be up pretty much instantly. (Unless you caught up.) So, how about machine learning? Do you think a fraud could land and keep such a job, without any knowledge, qualifications, ability, or even interest in getting up to speed? Just, a pure, simple fraud. What's your guess?
    - mjn 7y ago
      > Also, why is NEURIPS or ICML papers is not a hiring guarantee? I thought they're highly sought after. They're sought after, but the conferences have also grown huge. NeurIPS 2018 accepted around 1,000 papers! Based on a query of the DBLP [1] dataset, there were 4,409 distinct authors who had a paper at either NeurIPS 2018 or ICML 2018 (or both). If you add in a few of the other big AI and ML conferences (AAAI, IJCAI, ICLR), the number grows to 10,995 distinct authors, again solely for the year 2018. The field is hot, but is it hot enough for ten thousand people to be automatically hired because of one paper? There's also decreasing confidence in the big conferences' review processes I think. NeurIPS 2014 actually did a study to estimate how random acceptance was by assigning some papers to two different sets of reviewers and checking how similar the decisions were [2], and found there was a much higher degree of luck in acceptance/rejection decisions than they had expected. I personally have more confidence in the review processes of smaller and more focused conferences (and journals!), though they don't have the same level of name recognition. [1] https://dblp.uni-trier.de/ https://dblp.uni-trier.de/ [2] http://blog.mrtz.org/2014/12/15/the-nips-experiment.html http://blog.mrtz.org/2014/12/15/the-nips-experiment.html
      - throwawayjava 7y ago
        A Ph.D.'s worth of first authored NeurIPS/ICML papers will get you a very good job pretty easily still. But AI slices papers very thin and author lists are inflated relative to other subfields of CS. A single paper in one of the major conferences is a pretty marginal contribution, especially if you're in the middle of a long author list. Also, NeurIPS reviewing has gone to absolute hell. I mean, peer review everywhere has problems. But I've never seen something quite this bad. At this point I think it's safe to say that most reviewers wouldn't even make it to an on-site interview for a faculty position at a research university. That's definitely nowhere near normal. You can't really blame anyone, I guess; the community is growing way too quickly for any real quality control. Frankly, I think those conferences have outlived their usefulness as anything except marquee marketing events. I'm now mostly attending smaller and more specialized conferences.
        mjn 7y ago
        I've gone in a similar direction. Only at smaller conferences can you have any kind of confidence that your reviewers are people with actual expertise in the field. That's pretty useful, not only because it makes it less likely you'll get reviews that are very annoying, but also because a review by a knowledgeable person can be genuinely valuable. The big conferences are full of reviews written by 2nd-year grad students, because with this many submissions, any warm body with anything approaching credentials is needed. Besides just "quality" in the general sense, one thing this has really hurt, I think, is any sense of history or continuity. There are a ton of reviewers who have basically no familiarity with the pre-2010 ML literature, and it kind of shows in both the reviews and the papers that get published. I mean I get that deep learning beats a lot of older methods on major benchmarks, but it's still not the case that literally every problem, controversy, and technique was first studied post-2010.
  - rantwasp 7y ago
    Isn't this always the case though? A PhD is a long term investment and the field that you go into is probably not the one that will be hot when you finish your PhD. So driving your decisions on $$$ seems foolish at best.
    - mellosouls 7y ago
      Yep. If you're empty enough to follow a PhD solely for the money, there are far safer financial bets. Go into medicine or something. Not that I'd want to experience that bedside manner...
      - nefitty 7y ago
        It is not fair to call someone "empty" because their motivation stems from something you don't share. It does not make their work and contribution any less difficult or necessary.
        xpe 7y ago
        Though harsh and (of course) generalized, I think the word "empty" is a useful description here. Think of these meanings of empty: - "containing nothing; not filled or occupied" - "lacking meaning or sincerity" - "having no value or purpose" In the context of academia, there is an expectation of a certain fundamental motivation; namely, to pursue knowledge in a particular field and share it back via writing and/or teaching. If one lacks that primary motivation, they certainly "lack" it and are "not filled" with the academic ethos. So "empty" is a fair term. Now, I understand the realities as well -- seeking publications, recognition, tenure, and funding are also political activities. But this does not contradict the underlying community norm that I mention above. In fact, it supports it -- it explains why so many people endure a tough, grueling, political process despite it not being their wheelhouse.
        rat9988 7y ago
        >If one lacks that primary motivation, they certainly "lack" it and are "not filled" with the academic ethos. So "empty" is a fair term. No idea why you think this. A phd has a contrat with a salary, starting and end dates.
  - hdrujvw-4579 7y ago
    >I have my doubts there will be cushy FAANG jobs for everyone capable of playing around with network architectures in a few years. Presumably because technical needs of FAANG might be moving in other directions. Could someone comment why this might be the case, and what other fields might look relevant
    - sgt101 7y ago
      I think that the other force is that the skills that are being created by ML Ph.D programs get commodified. I don't think this will happen, because typing "import tensorflow.keras.*" isn't the skill that an ML Ph.D develops, and it is the part of the skill set that is (and will be) commodity along with the automl stuff. Constructing a problem, handling the data and running a proper process is harder, and it's the value that will put processes that use ML at risk, and deliver differentiating value for the ones where it works.
  - jcims 7y ago
    What about domain-specific applications of the technology? I've been in the information security industry for ~25 years and while 'AI' has been increasing in use but is still woefully short of its potential in a few different areas. It seems that someone with deep domain expertise that also has PhD level chops in ML would be a profit machine.
- hos234 7y ago
  $$$ always depends on who your prof knows, what connections the lab has with industry and the contacts you make.
- chubot 7y ago
  Remember that Hinton and other deep learning pioneers labored in obscurity for over a decade, probably closer to two. Techniques like deep learning were out of fashion for a long time. Google was making tons of money on non-AI techniques; I remember Sergey Brin saying he was surprised by the deep learning breakthroughs in 2012 because the conventional wisdom was that "AI doesn't work". And people who did Ph.D's under Hinton and others 10+ years ago also were not following trends. You have to take risk to get reward. The way to do that now would also be to labor on an obscure subfield of AI that isn't certain to even work, let alone become commercially viable. (And there are plenty of people who did that on fields that looked more promising than deep learning 10+ years ago.) There's definitely a need for new AI techniques, see this other current story: https://news.ycombinator.com/item?id=21104037 https://news.ycombinator.com/item?id=21104037
  - sandGorgon 7y ago
    exactly this. The people who are at the top of their field in anything (including AI) .. did it when nobody else was working on it.
  - jonbaer 7y ago
    Not to also mention that much of this would still be in the stone age if it were not for MUCH faster and accessible hardware (GPU/TPU/QPU/XPU), what I think makes up for the high salaries is that these people are quite aware of how to allocate for hardware on the ML/RL/AI training side and how to allocate time for the people responsible for tagging and optimizing models. They know how to reach the objective(s) in training/testing/optimizing already.
  - ilaksh 7y ago
    Personally I think deep learning is the new faith and only a true heretic will be able to make breakthroughs towards AGI.
- nharada 7y ago
  I do R&D in computer vision, and everyone is shocked when I tell them that the roles don't pay that much better than software engineering. Articles like these make people assume you can make 2-3x what you would as a software engineer at the same level of experience (remember a fresh PhD has ~5 years research experience), and it's usually not the case. The actual multiplier is something like 1.3-1.5x. I had one offer come in under the equivalent software role at the same company. I also suspect that other fields have similar patterns where a few experts make a ton of money, they're just not as publicized because the media is so hyped on AI. How much you think Jim Keller is making? TL;DR do the PhD because you love the research. If you want the money do some internships, land a FAANG job, and try and get to senior as fast as possible.
- eli_gottlieb 7y ago
  As someone presently doing a PhD in machine learning and neuroscience, which includes submitting to AI conferences... Frankly, I think this is a bubble, and don't expect to ever make anything like that much money, in academia or industry. Yes, deep learning has a lot of successes and industrial applications, but deep learning wizards are not nearly so impossible to replace or replicate. As the field settles more towards engineering than wizardry, and as expectations are recalibrated towards the realistic, it's going to stop raining cash.
  - HeWhoLurksLate 7y ago
    I hope that you live below your current means.
    - eli_gottlieb 7y ago
      I'm a grad student, so my current means are pretty modest, but, well, yeah. Not saving and paying down debts is insane in an economy that is either in a recession or about to be.
- hogFeast 7y ago
  If you want to earn money, create the most revenue. The problem that most technical people seem to have is that they think a qualification is a licence to print money. It isn't. The majority of people who obtain qualifications have no idea how to make money (that is why they are doing the qualification rather than making money, and this effect increases with the complexity of the course of study). So if you want to make money and your interest is AI, sure do a PhD...but at some point, you are going to have to work out how you can use those skills to sell something. I think, at some point, there is going to be a rude awakening. Right now, we this situation where you can tap up some VC idiot to pay your salary for a few years whilst you fuck about on some bullshit. You can get acquired by some megacorp with a ton of cash and have no accountability from shareholders while you light huge stacks of their cash on fire...this always ends. Always. You are either a bureaucrat chasing qualifications to get no-accountability jobs or you are someone that can create value for other people. I am not knocking any kind of advanced degree (I have one). But the point of it is not money. So even posing the question is totally misguided.
- ineedasername 7y ago
  I wouldn't make a Ph.D program decision based solely or even primarily based on potential salary. Aptitude and, especially, personal interest should be a stronger guide. AI will likely turn out to be as data science was 7 or 8 years ago: In demand, for now. Then, the market will be flooded and salaries will drop to the average rates for qualified professionals of a given level of credentials & experience.
- TheCoelacanth 7y ago
  Given that even the second highest paid researcher at the organization doesn't make that much, I'm guessing not very easily replicable.
rm999 7y ago
> A third big name in the field, the roboticist Pieter Abbeel, made $425,000, though he did not join until June 2016, after taking a leave from his job as a professor at the University of California, Berkeley. Those figures all include signing bonuses. 425k after signing bonus is probably closer to 300-350k base + annual bonus, which feels low for someone in such a distinguished position (close to a typical specialized physician or big tech software engineer with 5-10 years of experience). After finishing my masters degree in machine learning in the mid-2000s, I immediately got job offers higher than my much more talented professors. To me, the real story is that academia has set the compensation bar low for people adding so much value. Edit: to be clear I know these salaries are high and people in my field are lucky to be making so much. But I also don’t think it’s accurate that people in the field are overpaid, which titles like OP may make it seem.
- ychen306 7y ago
  > , though he did not join until June 2016
  - ec109685 7y ago
    So his effective salary is double that.
    - zawerf 7y ago
      I think people are supposed to report their salary with the stock price at grant, not after appreciation (at least on levels.fyi). But yea it's a lot more because google stock is half their comp and it keeps doubling every 3-4 years.
      - Zaheer 7y ago
        I’m from Levels.fyi. We actually request users to report compensation with appreciation. This is because the appreciation can actually be used to negotiate compensation elsewhere and provides a more accurate picture.
- deleted 7y ago
rdiddly 7y ago
[2018]
jorblumesea 7y ago
Serious question, is this value justified? I don't have any contact with AI development or AI researchers. Do they really bring that much value to the business? It makes sense that for Google, these positions would have almost unlimited clean data. But many companies are not Google.
- m0zg 7y ago
  How do you define "justified"? The field is so new, someone who really knows what they're doing could mean a difference between a large team spending years and getting nowhere remarkable, and improving state of the art every 6 months. With the same exact team. So would you rather get nothing for your money, or something valuable every 6-9 months? That's the question. These people are typically hired after trying pretty hard to get results out of generalists, and failing.
  - scarejunba 7y ago
    I think he's asking if these people produce positive marginal value to the company. The answer is usually yes.
    - deleted 7y ago
      [deleted]
- ryacko 7y ago
  I don't think AT&T made much from Bell Labs.
  - HeWhoLurksLate 7y ago
    Directly.
    - ryacko 7y ago
      Unix-compatibility has yielded more income to other companies than to the company that designed it.
currymj 7y ago
Misleading article and headline; a few celebrity researchers in high-up positions make this much. You can probably find some chemists who make $1M too if they have made it to executive positions. Most AI researchers in industry make a lot of money, $200k+, but that is not so outrageous in the context of big tech companies. And in fact the vast majority of AI researchers are making $20k-$30k a year, because they are graduate students.
- Railsify 7y ago
  And if they live in California 200k does not seem like a lot.
  - maccard 7y ago
    Assuming 4k/month on rent, your leftover pay after taxes and rent is more than most senior engineers gross in the rest of the US/Western Europe. 200k is a lot.
  - jstummbillig 7y ago
    If the context simply is "median California household income", then it should seem like a lot, because it really still is a lot. https://en.wikipedia.org/wiki/List_of_California_locations_by_income https://en.wikipedia.org/wiki/List_of_California_locations_b...
    - ModernMech 7y ago
      $200k in the bay area is like $90k where I'm from. It's a decent living, but if you've got a family it's not exactly upper class.
      - ryanSrich 7y ago
        If you have a family it’s not possible. Home prices average close to $1m. You’d have to save an entire years salary PRE tax to afford the down payment. That will take years and years to save. If you’re raising a family you’ll likely need two $200k incomes to afford a house, and even then, it’ll take at least 2 years to save for the down payment. The Bay Area is for college kids and millionaires. That’s it.
aledalgrande 7y ago
I think this article just reeks of jealousy. What is wrong with paying people doing top research a top salary? Their skills are extremely rare. And I bet executives get way more anyway.
- AdrianB1 7y ago
  It is New York Times, the egalitarian tribune; socialists don't like rich people and by extension anyone that earns more than average.
  - dang 7y ago
    Please don't take HN threads further into ideological flamewar. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- scottlegrand2 7y ago
  What's amazing to me is the same people who write articles like this don't blink at the salaries in Hollywood. A top-tier performer can get over a hundred million dollars per movie. A bottom-tier character actor or below the line talent barely gets by. The difference in compensation here is nowhere near as bad as what happens in Hollywood.
  - harry8 7y ago
    Without commenting on quality of the journalism here nor making any judgements about the normative economics [1] of the situation described: Hollywood actors salaries (or those of sports stars, or CEOs, or authors of best sellers or pop stars) are a known thing so you don't write a 'news' story about the magnitude they have obtained. AI researchers making large sums is not widely known so is 'news' and of interest to readers. [1] https://www.investopedia.com/terms/n/normativeeconomics.asp https://www.investopedia.com/terms/n/normativeeconomics.asp
make3 7y ago
Please add (2018) to the title. Also, these are celebrities, with some of the most important contributions in the field. They are like the top 0.001% of the field or something equally ridiculous.
seibelj 7y ago
Computer science researchers should form partnerships like lawyers and doctors. They could extract far, far more value out of mega corps than they get paid as salary employees. We are at a moment when AI tech is so black box that they could create a company that is itself a black box.
- buboard 7y ago
  This should work for all VC-funded companies and for the internet giants who can afford it. It's a form of wealth redistribution.
paulpauper 7y ago
What sport of math is needed to learn this stuff? Is it linear algebra and some deferential calculus mostly?
scottlegrand2 7y ago
7-figure salaries have been a thing for quite a while at Facebook, Google and definitely at hedge funds. Right now, you can make a half million a year or more if you're willing to optimize the low-level code in any of the major AI frameworks. If you publish a major conference paper, 7 figures is pretty easy to attain. You don't have to be in the 0.001% just the top 5%. That's hard work but it's doable. The only thing that's remotely fungible in AI is python-based data scientists who can do little more than port new data to existing models open sourced on GitHub. And even then they do pretty well because of the huge demand. I don't see that changing anytime soon. I believe expecting automated systems to replace data scientists is the same level of naivete as believing we'd reach L5 autonomy in a few years in 2014. Anthony Lewandowski was making 120 million dollars a year when he left Waymo. If you're an AI expert in the valley and you're not making at least $500k, you are one job hop away from doing so if you play your cards remotely right.
- amelius 7y ago
  > python-based data scientists who can do little more than port new data to existing models open sourced on GitHub The data-scientist is the new web-developer.
- HenryKissinger 7y ago
  > If you publish a major conference paper, 7 figures is pretty easy to attain. How does it work in practice? Let's say I just published a major conference paper. Where do I apply for my million dollar job? Who do I reach out to?
  - nharada 7y ago
    You don't know? Usually at NeurIPS and CVPR the applications are next to the tea and coffee they put out in the mornings. Not sure about ICML.
    - HenryKissinger 7y ago
      I did not know about these conferences. I googled the first one and found a headline about how tickets sold out in minutes. Is this why? Because everyone attending hopes to get a 6 figures job?
xchaotic 7y ago
Just FYI I made $millions doing CRUD apps for enterprise. I spent most of it on accommodation and food so not as sexy as AI I guess
- person_of_color 7y ago
  What was your strategy?
drumttocs8 7y ago
Is it worth putting the time into learning AI/ML at this point? Sure, crazy high salaries- but it seems inevitable that these are the very jobs that will be automated away first.
- lapaz17 7y ago
  Why do you think they will automated first?
- dwrodri 7y ago
  The crazy high salaries are "ideally" going to the ones who have both the soft skills and the technical prowess to automate away other people's jobs, not their own. This is one of the reasons STEM PhDs have become so attractive to industry--getting a PhD (especially from a good school) is a certification that the individual is capable of producing previously undocumented methods for replacing other people's jobs.
capnprotonmail 7y ago
I don't think OpenAI is a nonprofit as they are planning to commercially license their technologies. Ok, they limit investor returns at one-hundred times their initial investment, but I'd hardly call that a non-profit in the sense of the word.
- hollerith 7y ago
  Wikipedia says that "OpenAI is the for-profit corporation OpenAI LP, whose parent organization is the non-profit organization OpenAI Inc."
  - _delirium 7y ago
    It's kind of a weird structure, but I think more like a company than like a nonprofit in the short/medium term. The parent is a nonprofit organization, but it's not the same arrangement as Mozilla Foundation vs. Mozilla Corporation, where all earnings from the subsidiary go to the parent nonprofit. With OpenAI, the parent nonprofit only gets earnings in excess of 100x return. The for-profit company raises investment and returns any earnings to the investors until/unless they hit the 100x return mark. They call this a "capped profit" structure. When it was founded in 2015, OpenAI was originally a nonprofit, but restructured into this new arrangement earlier this year in order to be able to raise VC money: https://techcrunch.com/2019/03/11/openai-shifts-from-nonprofit-to-capped-profit-to-attract-capital/ https://techcrunch.com/2019/03/11/openai-shifts-from-nonprof...
- deleted 7y ago
  [deleted]
beamatronic 7y ago
This ($1m total comp) is becoming more and more common. I know of a guy making $650k base and $400k in RSUs first year. Just a Java individual contributor, but with a reputation and buddies high up.
- mercutio2 7y ago
  A $400k RSU grant is not $400k towards total compensation in the first year. RSUs generally vest over 3-4 years, so at the end of the first 12-13 months, it’s another $100k towards total comp. You may or may not receive additional grants in future years. If you get $400k grants every year, you might get up to $1M/year, but not immediately.
  - beamatronic 7y ago
    This was monthly RSU grants vesting from day 1.
hyperbovine 7y ago
This is a year and a half old. I wonder to what extent it has still true now that the AI hype tsunami has receded ever so slightly.
rsp1984 7y ago
(2018)
ilaksh 7y ago
$2 million per year is very low for people at that level if you think about the potential profits that advanced AI can bring and compare the real world utility to that of, for example, a celebrity or professional sports player. They are at least talking about AGI. If those people get anywhere close to AGI, they should start charging ten or hundred times more, because having that kind of technology could be worth hundreds of billions or more. So even though there is no indication they are close to it, if there is even a remote possibility of getting there, the salaries are questionable in the context of those potential astronomical profits.
anon91831837 7y ago
If you wanted to command, let's say $5-8 megabucks per year conservatively, I would sell the idea of self-programming systems. Automating software development to not need as many humans and implement prototype systems more rapidly is a business holy grail because it could significantly reduce the need for as many developers and their relatively large salaries. It will happen eventually, it's just a matter of time and who's going to profit that's the question. Furthermore, as automation intensifies over time, there will be overall fewer jobs but their salaries will continue to increase if they are related to automation and further reduction of jobs. If I were an AI researcher, I would be in self-programming systems, insist on patent assignment and consider forming a startup to deliver it as a hybrid cloud/on-prem SaaS... this would be the most obvious path to get rich from it.
gumby 7y ago
(2018) and discussed at the time

How replicable is this thing? Looking from a monetary perspective, is it worth getting a Ph.D. in AI? Assuming you like both academic research and working in the industry, if you were to make a choice whether to do a Ph.D. or not, how would you make it?

high_derivative 7y ago
No, it's not. Im an AI researcher at a big tech company. It would have been far wiser financially to start as a software engineer after my Bachelor's. Also, having a few NEURIPS or ICML papers is not a hiring guarantee any more. It's decidedly not 2015-17 any longer. In particular, I feel bad for the people who started their PhDs with $$$ in their eyes around that time. Don't get me wrong, they will all be employable, but the field moves so fast, I have my doubts there will be cushy FAANG jobs for everyone capable of playing around with network architectures in a few years. It's a terrible idea to start one based on the hype few years ago.
- alphagrep12345 7y ago
  I have a couple of questions - - Would you say that you'd have got an AI researcher position without a Ph.D.? - Also, why is NEURIPS or ICML papers is not a hiring guarantee? I thought they're highly sought after.
  - joshvm 7y ago
    > Would you say that you'd have got an AI researcher position without a Ph.D.? It's difficult to get any true researcher position without a PhD. It doesn't mean that PhD has to be in AI. Research involves a lot of reading and writing papers, which a PhD is supposedly training you how to do. That said most places will say "equivalent practical experience" and it's entirely possible to be competent in AI/ML without a PhD. I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. The interesting jobs (to me) are where domains cross, and it's also (hint hint) much easier to get a job doing AI for X than it is doing "fundamental AI". In any case, you're often doing stuff that nobody has done before anyway, but you don't need to spend your life hunting for the new ResNet. > Also, why is NEURIPS or ICML papers is not a hiring guarantee? What the OP probably might be implying is that everyone has a publication in NeurIPS nowadays. I think it goes deeper than that though, publishing in machine learning is broken. Having 10k people at one conference is not an efficient way to distribute research. You have to submit a full paper in November for a conference next Summer - pretty much only computer science does this madness. What's interesting is how unique this attitude is. In astronomy, for example, conferences are a fun place to catch up with folks in your niche. There might be a few hundred people and probably it'll be single-track. We publish whatever journal is the most relevant and they're generally all considered equivalent. Nobody cares if you publish in ApJ vs A&A vs MNRAS, if your research is good. There are also concerns that the quality of these venues is decreasing because the pressure to publish in them is so high. See https://arxiv.org/abs/1807.03341 https://arxiv.org/abs/1807.03341
    - neuralsp 7y ago
      >I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. Do you think it is possible to that without any background in anything? I mean could someone apply black box frameworks without understanding them. How would they be caught?
      - joshvm 7y ago
        > Do you think it is possible to that without any background in anything? To do machine learning research? Or work in some random domain? > I mean could someone apply black box frameworks without understanding them. How would they be caught? Machine learning is rapidly becoming commoditised, but lots of people still don't understand just how much effort it is to get a good dataset and to prep Domain experts scoff at machine learning people who are trying to solve Big Problems using unrepresentative toy datasets, but also tend to have much higher expectations of what ML can do. Machine learning people scoff at domain experts for using outdated techniques and bad data science, but then propose ridiculous solutions that would never work in the real world (e.g. use our model, it takes a week on 8xV100s to train and you can only run it on a computer the size of a bus). There are also a lot of people (and companies) touting machine learning as a solution to problems that don't exist. Overfitting models is probably the most rampant crime that researchers commit.
        neuralsp 7y ago
        My question is whether someone could fake it and not be caught/fired. (So yes, I meant: "Or work in some random domain?") From the second half of your comment it seems that the answer is yes? Maybe a comparison would help: someone pretending to be an experienced iOS/Android developer without any qualifications or ability would quickly be caught. Since they couldn't produce any working app or use a compiler, and anyone can judge an app for themselves. You can't really just make it up out of whole cloth, people judge the results. You would have to start actually doing that, and if you couldn't or didn't want to, then unless you outsourced your own job or something the jig would be up pretty much instantly. (Unless you caught up.) So, how about machine learning? Do you think a fraud could land and keep such a job, without any knowledge, qualifications, ability, or even interest in getting up to speed? Just, a pure, simple fraud. What's your guess?
  - mjn 7y ago
    > Also, why is NEURIPS or ICML papers is not a hiring guarantee? I thought they're highly sought after. They're sought after, but the conferences have also grown huge. NeurIPS 2018 accepted around 1,000 papers! Based on a query of the DBLP [1] dataset, there were 4,409 distinct authors who had a paper at either NeurIPS 2018 or ICML 2018 (or both). If you add in a few of the other big AI and ML conferences (AAAI, IJCAI, ICLR), the number grows to 10,995 distinct authors, again solely for the year 2018. The field is hot, but is it hot enough for ten thousand people to be automatically hired because of one paper? There's also decreasing confidence in the big conferences' review processes I think. NeurIPS 2014 actually did a study to estimate how random acceptance was by assigning some papers to two different sets of reviewers and checking how similar the decisions were [2], and found there was a much higher degree of luck in acceptance/rejection decisions than they had expected. I personally have more confidence in the review processes of smaller and more focused conferences (and journals!), though they don't have the same level of name recognition. [1] https://dblp.uni-trier.de/ https://dblp.uni-trier.de/ [2] http://blog.mrtz.org/2014/12/15/the-nips-experiment.html http://blog.mrtz.org/2014/12/15/the-nips-experiment.html
    - throwawayjava 7y ago
      A Ph.D.'s worth of first authored NeurIPS/ICML papers will get you a very good job pretty easily still. But AI slices papers very thin and author lists are inflated relative to other subfields of CS. A single paper in one of the major conferences is a pretty marginal contribution, especially if you're in the middle of a long author list. Also, NeurIPS reviewing has gone to absolute hell. I mean, peer review everywhere has problems. But I've never seen something quite this bad. At this point I think it's safe to say that most reviewers wouldn't even make it to an on-site interview for a faculty position at a research university. That's definitely nowhere near normal. You can't really blame anyone, I guess; the community is growing way too quickly for any real quality control. Frankly, I think those conferences have outlived their usefulness as anything except marquee marketing events. I'm now mostly attending smaller and more specialized conferences.
      - mjn 7y ago
        I've gone in a similar direction. Only at smaller conferences can you have any kind of confidence that your reviewers are people with actual expertise in the field. That's pretty useful, not only because it makes it less likely you'll get reviews that are very annoying, but also because a review by a knowledgeable person can be genuinely valuable. The big conferences are full of reviews written by 2nd-year grad students, because with this many submissions, any warm body with anything approaching credentials is needed. Besides just "quality" in the general sense, one thing this has really hurt, I think, is any sense of history or continuity. There are a ton of reviewers who have basically no familiarity with the pre-2010 ML literature, and it kind of shows in both the reviews and the papers that get published. I mean I get that deep learning beats a lot of older methods on major benchmarks, but it's still not the case that literally every problem, controversy, and technique was first studied post-2010.
- rantwasp 7y ago
  Isn't this always the case though? A PhD is a long term investment and the field that you go into is probably not the one that will be hot when you finish your PhD. So driving your decisions on $$$ seems foolish at best.
  - mellosouls 7y ago
    Yep. If you're empty enough to follow a PhD solely for the money, there are far safer financial bets. Go into medicine or something. Not that I'd want to experience that bedside manner...
    - nefitty 7y ago
      It is not fair to call someone "empty" because their motivation stems from something you don't share. It does not make their work and contribution any less difficult or necessary.
      - xpe 7y ago
        Though harsh and (of course) generalized, I think the word "empty" is a useful description here. Think of these meanings of empty: - "containing nothing; not filled or occupied" - "lacking meaning or sincerity" - "having no value or purpose" In the context of academia, there is an expectation of a certain fundamental motivation; namely, to pursue knowledge in a particular field and share it back via writing and/or teaching. If one lacks that primary motivation, they certainly "lack" it and are "not filled" with the academic ethos. So "empty" is a fair term. Now, I understand the realities as well -- seeking publications, recognition, tenure, and funding are also political activities. But this does not contradict the underlying community norm that I mention above. In fact, it supports it -- it explains why so many people endure a tough, grueling, political process despite it not being their wheelhouse.
        rat9988 7y ago
        >If one lacks that primary motivation, they certainly "lack" it and are "not filled" with the academic ethos. So "empty" is a fair term. No idea why you think this. A phd has a contrat with a salary, starting and end dates.
- hdrujvw-4579 7y ago
  >I have my doubts there will be cushy FAANG jobs for everyone capable of playing around with network architectures in a few years. Presumably because technical needs of FAANG might be moving in other directions. Could someone comment why this might be the case, and what other fields might look relevant
  - sgt101 7y ago
    I think that the other force is that the skills that are being created by ML Ph.D programs get commodified. I don't think this will happen, because typing "import tensorflow.keras.*" isn't the skill that an ML Ph.D develops, and it is the part of the skill set that is (and will be) commodity along with the automl stuff. Constructing a problem, handling the data and running a proper process is harder, and it's the value that will put processes that use ML at risk, and deliver differentiating value for the ones where it works.
- jcims 7y ago
  What about domain-specific applications of the technology? I've been in the information security industry for ~25 years and while 'AI' has been increasing in use but is still woefully short of its potential in a few different areas. It seems that someone with deep domain expertise that also has PhD level chops in ML would be a profit machine.
hos234 7y ago
$$$ always depends on who your prof knows, what connections the lab has with industry and the contacts you make.
chubot 7y ago
Remember that Hinton and other deep learning pioneers labored in obscurity for over a decade, probably closer to two. Techniques like deep learning were out of fashion for a long time. Google was making tons of money on non-AI techniques; I remember Sergey Brin saying he was surprised by the deep learning breakthroughs in 2012 because the conventional wisdom was that "AI doesn't work". And people who did Ph.D's under Hinton and others 10+ years ago also were not following trends. You have to take risk to get reward. The way to do that now would also be to labor on an obscure subfield of AI that isn't certain to even work, let alone become commercially viable. (And there are plenty of people who did that on fields that looked more promising than deep learning 10+ years ago.) There's definitely a need for new AI techniques, see this other current story: https://news.ycombinator.com/item?id=21104037 https://news.ycombinator.com/item?id=21104037
- sandGorgon 7y ago
  exactly this. The people who are at the top of their field in anything (including AI) .. did it when nobody else was working on it.
- jonbaer 7y ago
  Not to also mention that much of this would still be in the stone age if it were not for MUCH faster and accessible hardware (GPU/TPU/QPU/XPU), what I think makes up for the high salaries is that these people are quite aware of how to allocate for hardware on the ML/RL/AI training side and how to allocate time for the people responsible for tagging and optimizing models. They know how to reach the objective(s) in training/testing/optimizing already.
- ilaksh 7y ago
  Personally I think deep learning is the new faith and only a true heretic will be able to make breakthroughs towards AGI.
nharada 7y ago
I do R&D in computer vision, and everyone is shocked when I tell them that the roles don't pay that much better than software engineering. Articles like these make people assume you can make 2-3x what you would as a software engineer at the same level of experience (remember a fresh PhD has ~5 years research experience), and it's usually not the case. The actual multiplier is something like 1.3-1.5x. I had one offer come in under the equivalent software role at the same company. I also suspect that other fields have similar patterns where a few experts make a ton of money, they're just not as publicized because the media is so hyped on AI. How much you think Jim Keller is making? TL;DR do the PhD because you love the research. If you want the money do some internships, land a FAANG job, and try and get to senior as fast as possible.
eli_gottlieb 7y ago
As someone presently doing a PhD in machine learning and neuroscience, which includes submitting to AI conferences... Frankly, I think this is a bubble, and don't expect to ever make anything like that much money, in academia or industry. Yes, deep learning has a lot of successes and industrial applications, but deep learning wizards are not nearly so impossible to replace or replicate. As the field settles more towards engineering than wizardry, and as expectations are recalibrated towards the realistic, it's going to stop raining cash.
- HeWhoLurksLate 7y ago
  I hope that you live below your current means.
  - eli_gottlieb 7y ago
    I'm a grad student, so my current means are pretty modest, but, well, yeah. Not saving and paying down debts is insane in an economy that is either in a recession or about to be.
hogFeast 7y ago
If you want to earn money, create the most revenue. The problem that most technical people seem to have is that they think a qualification is a licence to print money. It isn't. The majority of people who obtain qualifications have no idea how to make money (that is why they are doing the qualification rather than making money, and this effect increases with the complexity of the course of study). So if you want to make money and your interest is AI, sure do a PhD...but at some point, you are going to have to work out how you can use those skills to sell something. I think, at some point, there is going to be a rude awakening. Right now, we this situation where you can tap up some VC idiot to pay your salary for a few years whilst you fuck about on some bullshit. You can get acquired by some megacorp with a ton of cash and have no accountability from shareholders while you light huge stacks of their cash on fire...this always ends. Always. You are either a bureaucrat chasing qualifications to get no-accountability jobs or you are someone that can create value for other people. I am not knocking any kind of advanced degree (I have one). But the point of it is not money. So even posing the question is totally misguided.
ineedasername 7y ago
I wouldn't make a Ph.D program decision based solely or even primarily based on potential salary. Aptitude and, especially, personal interest should be a stronger guide. AI will likely turn out to be as data science was 7 or 8 years ago: In demand, for now. Then, the market will be flooded and salaries will drop to the average rates for qualified professionals of a given level of credentials & experience.
TheCoelacanth 7y ago
Given that even the second highest paid researcher at the organization doesn't make that much, I'm guessing not very easily replicable.

rm999 7y ago

> A third big name in the field, the roboticist Pieter Abbeel, made $425,000, though he did not join until June 2016, after taking a leave from his job as a professor at the University of California, Berkeley. Those figures all include signing bonuses. 425k after signing bonus is probably closer to 300-350k base + annual bonus, which feels low for someone in such a distinguished position (close to a typical specialized physician or big tech software engineer with 5-10 years of experience). After finishing my masters degree in machine learning in the mid-2000s, I immediately got job offers higher than my much more talented professors. To me, the real story is that academia has set the compensation bar low for people adding so much value. Edit: to be clear I know these salaries are high and people in my field are lucky to be making so much. But I also don’t think it’s accurate that people in the field are overpaid, which titles like OP may make it seem.

ychen306 7y ago
> , though he did not join until June 2016
- ec109685 7y ago
  So his effective salary is double that.
  - zawerf 7y ago
    I think people are supposed to report their salary with the stock price at grant, not after appreciation (at least on levels.fyi). But yea it's a lot more because google stock is half their comp and it keeps doubling every 3-4 years.
    - Zaheer 7y ago
      I’m from Levels.fyi. We actually request users to report compensation with appreciation. This is because the appreciation can actually be used to negotiate compensation elsewhere and provides a more accurate picture.
deleted 7y ago

rdiddly 7y ago

[2018]

jorblumesea 7y ago

Serious question, is this value justified? I don't have any contact with AI development or AI researchers. Do they really bring that much value to the business? It makes sense that for Google, these positions would have almost unlimited clean data. But many companies are not Google.

m0zg 7y ago
How do you define "justified"? The field is so new, someone who really knows what they're doing could mean a difference between a large team spending years and getting nowhere remarkable, and improving state of the art every 6 months. With the same exact team. So would you rather get nothing for your money, or something valuable every 6-9 months? That's the question. These people are typically hired after trying pretty hard to get results out of generalists, and failing.
- scarejunba 7y ago
  I think he's asking if these people produce positive marginal value to the company. The answer is usually yes.
  - deleted 7y ago
    [deleted]
ryacko 7y ago
I don't think AT&T made much from Bell Labs.
- HeWhoLurksLate 7y ago
  Directly.
  - ryacko 7y ago
    Unix-compatibility has yielded more income to other companies than to the company that designed it.

currymj 7y ago

Misleading article and headline; a few celebrity researchers in high-up positions make this much. You can probably find some chemists who make $1M too if they have made it to executive positions. Most AI researchers in industry make a lot of money, $200k+, but that is not so outrageous in the context of big tech companies. And in fact the vast majority of AI researchers are making $20k-$30k a year, because they are graduate students.

Railsify 7y ago
And if they live in California 200k does not seem like a lot.
- maccard 7y ago
  Assuming 4k/month on rent, your leftover pay after taxes and rent is more than most senior engineers gross in the rest of the US/Western Europe. 200k is a lot.
- jstummbillig 7y ago
  If the context simply is "median California household income", then it should seem like a lot, because it really still is a lot. https://en.wikipedia.org/wiki/List_of_California_locations_by_income https://en.wikipedia.org/wiki/List_of_California_locations_b...
  - ModernMech 7y ago
    $200k in the bay area is like $90k where I'm from. It's a decent living, but if you've got a family it's not exactly upper class.
    - ryanSrich 7y ago
      If you have a family it’s not possible. Home prices average close to $1m. You’d have to save an entire years salary PRE tax to afford the down payment. That will take years and years to save. If you’re raising a family you’ll likely need two $200k incomes to afford a house, and even then, it’ll take at least 2 years to save for the down payment. The Bay Area is for college kids and millionaires. That’s it.

aledalgrande 7y ago

I think this article just reeks of jealousy. What is wrong with paying people doing top research a top salary? Their skills are extremely rare. And I bet executives get way more anyway.

AdrianB1 7y ago
It is New York Times, the egalitarian tribune; socialists don't like rich people and by extension anyone that earns more than average.
- dang 7y ago
  Please don't take HN threads further into ideological flamewar. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
scottlegrand2 7y ago
What's amazing to me is the same people who write articles like this don't blink at the salaries in Hollywood. A top-tier performer can get over a hundred million dollars per movie. A bottom-tier character actor or below the line talent barely gets by. The difference in compensation here is nowhere near as bad as what happens in Hollywood.
- harry8 7y ago
  Without commenting on quality of the journalism here nor making any judgements about the normative economics [1] of the situation described: Hollywood actors salaries (or those of sports stars, or CEOs, or authors of best sellers or pop stars) are a known thing so you don't write a 'news' story about the magnitude they have obtained. AI researchers making large sums is not widely known so is 'news' and of interest to readers. [1] https://www.investopedia.com/terms/n/normativeeconomics.asp https://www.investopedia.com/terms/n/normativeeconomics.asp

make3 7y ago

Please add (2018) to the title. Also, these are celebrities, with some of the most important contributions in the field. They are like the top 0.001% of the field or something equally ridiculous.

seibelj 7y ago

Computer science researchers should form partnerships like lawyers and doctors. They could extract far, far more value out of mega corps than they get paid as salary employees. We are at a moment when AI tech is so black box that they could create a company that is itself a black box.

buboard 7y ago
This should work for all VC-funded companies and for the internet giants who can afford it. It's a form of wealth redistribution.

paulpauper 7y ago

What sport of math is needed to learn this stuff? Is it linear algebra and some deferential calculus mostly?

scottlegrand2 7y ago

7-figure salaries have been a thing for quite a while at Facebook, Google and definitely at hedge funds. Right now, you can make a half million a year or more if you're willing to optimize the low-level code in any of the major AI frameworks. If you publish a major conference paper, 7 figures is pretty easy to attain. You don't have to be in the 0.001% just the top 5%. That's hard work but it's doable. The only thing that's remotely fungible in AI is python-based data scientists who can do little more than port new data to existing models open sourced on GitHub. And even then they do pretty well because of the huge demand. I don't see that changing anytime soon. I believe expecting automated systems to replace data scientists is the same level of naivete as believing we'd reach L5 autonomy in a few years in 2014. Anthony Lewandowski was making 120 million dollars a year when he left Waymo. If you're an AI expert in the valley and you're not making at least $500k, you are one job hop away from doing so if you play your cards remotely right.

amelius 7y ago
> python-based data scientists who can do little more than port new data to existing models open sourced on GitHub The data-scientist is the new web-developer.
HenryKissinger 7y ago
> If you publish a major conference paper, 7 figures is pretty easy to attain. How does it work in practice? Let's say I just published a major conference paper. Where do I apply for my million dollar job? Who do I reach out to?
- nharada 7y ago
  You don't know? Usually at NeurIPS and CVPR the applications are next to the tea and coffee they put out in the mornings. Not sure about ICML.
  - HenryKissinger 7y ago
    I did not know about these conferences. I googled the first one and found a headline about how tickets sold out in minutes. Is this why? Because everyone attending hopes to get a 6 figures job?

xchaotic 7y ago

Just FYI I made $millions doing CRUD apps for enterprise. I spent most of it on accommodation and food so not as sexy as AI I guess

person_of_color 7y ago
What was your strategy?

drumttocs8 7y ago

Is it worth putting the time into learning AI/ML at this point? Sure, crazy high salaries- but it seems inevitable that these are the very jobs that will be automated away first.

lapaz17 7y ago
Why do you think they will automated first?
dwrodri 7y ago
The crazy high salaries are "ideally" going to the ones who have both the soft skills and the technical prowess to automate away other people's jobs, not their own. This is one of the reasons STEM PhDs have become so attractive to industry--getting a PhD (especially from a good school) is a certification that the individual is capable of producing previously undocumented methods for replacing other people's jobs.

capnprotonmail 7y ago

I don't think OpenAI is a nonprofit as they are planning to commercially license their technologies. Ok, they limit investor returns at one-hundred times their initial investment, but I'd hardly call that a non-profit in the sense of the word.

hollerith 7y ago
Wikipedia says that "OpenAI is the for-profit corporation OpenAI LP, whose parent organization is the non-profit organization OpenAI Inc."
- _delirium 7y ago
  It's kind of a weird structure, but I think more like a company than like a nonprofit in the short/medium term. The parent is a nonprofit organization, but it's not the same arrangement as Mozilla Foundation vs. Mozilla Corporation, where all earnings from the subsidiary go to the parent nonprofit. With OpenAI, the parent nonprofit only gets earnings in excess of 100x return. The for-profit company raises investment and returns any earnings to the investors until/unless they hit the 100x return mark. They call this a "capped profit" structure. When it was founded in 2015, OpenAI was originally a nonprofit, but restructured into this new arrangement earlier this year in order to be able to raise VC money: https://techcrunch.com/2019/03/11/openai-shifts-from-nonprofit-to-capped-profit-to-attract-capital/ https://techcrunch.com/2019/03/11/openai-shifts-from-nonprof...
deleted 7y ago
[deleted]

beamatronic 7y ago

This ($1m total comp) is becoming more and more common. I know of a guy making $650k base and $400k in RSUs first year. Just a Java individual contributor, but with a reputation and buddies high up.

mercutio2 7y ago
A $400k RSU grant is not $400k towards total compensation in the first year. RSUs generally vest over 3-4 years, so at the end of the first 12-13 months, it’s another $100k towards total comp. You may or may not receive additional grants in future years. If you get $400k grants every year, you might get up to $1M/year, but not immediately.
- beamatronic 7y ago
  This was monthly RSU grants vesting from day 1.

hyperbovine 7y ago

This is a year and a half old. I wonder to what extent it has still true now that the AI hype tsunami has receded ever so slightly.

rsp1984 7y ago

(2018)

ilaksh 7y ago

$2 million per year is very low for people at that level if you think about the potential profits that advanced AI can bring and compare the real world utility to that of, for example, a celebrity or professional sports player. They are at least talking about AGI. If those people get anywhere close to AGI, they should start charging ten or hundred times more, because having that kind of technology could be worth hundreds of billions or more. So even though there is no indication they are close to it, if there is even a remote possibility of getting there, the salaries are questionable in the context of those potential astronomical profits.

anon91831837 7y ago

If you wanted to command, let's say $5-8 megabucks per year conservatively, I would sell the idea of self-programming systems. Automating software development to not need as many humans and implement prototype systems more rapidly is a business holy grail because it could significantly reduce the need for as many developers and their relatively large salaries. It will happen eventually, it's just a matter of time and who's going to profit that's the question. Furthermore, as automation intensifies over time, there will be overall fewer jobs but their salaries will continue to increase if they are related to automation and further reduction of jobs. If I were an AI researcher, I would be in self-programming systems, insist on patent assignment and consider forming a startup to deliver it as a hybrid cloud/on-prem SaaS... this would be the most obvious path to get rich from it.

gumby 7y ago

(2018) and discussed at the time