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The Machine Learning Job Market

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Re: The Machine Learning Job Market

#171

Post by anonymousDan »

ccmonnett wrote: post #38
melling wrote: post #9

Earlier quoted context omitted.

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?

Especially if you have Python experience, then yes the opportunity definitely exists. For example when I hire MLEs (which I am doing now if anyone wants to apply - supportlogic.io) I am willing to look at people who are solid Python/backend engineers and who have been "ML adjacent" or who we believe could learn the ropes of ML enough to contribute. The stronger an engineer, the more flexibility we have in ML knowledg…

Interesting. Honestly to me Python and backend engineer are effectively orthogonal skillsets though. I would expect any decent programmer to pick up Python in about a week... (slight exaggeration but you get the point).

Re: The Machine Learning Job Market

#172

Post by marcinzm »

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 was a VP at a startup (~200 people) before I was 30 without a PhD. It was all BS and I had less manager qualifications than a FAANG line manager. I got lucky.

It's clear from the blog post that the author is in the same boat. They lament CEOs not having time to do research but took a VP position. An actual VP doesn't have time do research so they're clearly not an actual VP. So they're likely a tech lead with an inflated title.

Re: The Machine Learning Job Market

#173

Post by uoaei »

pbronez wrote: post #123
uoaei wrote: post #106

Earlier quoted context omitted.

But it undeniably misleads the reader into thinking this person has held senior responsibilities since 2016, which is outright false, and may instill in the reader more confidence than is due.

As a hiring manager, I don't assume that. When there's a single title over a long range of time, I assume it's a terminal title. I look to the details for the position to see what kinds of work they've done. In the interview, I'll dig into trajectory and experience at various levels.

Then how am I to communicate to you, the hiring manager, that I held significant responsibilities for a longer period of time (eg 6 years) than an applicant who ducked out the moment their new title kicked in (eg 6 weeks)?

How much does that matter to you, anyway?

Re: The Machine Learning Job Market

#174

Post by sfriedr »

I work in theoretical machine learning and what I don't like about this piece is how casually the author assumes that what goes us to the present state (hacky approaches where you converge to something "good enough", unless the model is very simple, e.g. a feedforward net---and even here there are still open questions---and you have some theoretical guarantee) will also get us to "AGI".

I believe that is unlikely. Here's metaphor for that, that I often like to use when I speak to engineers doing empirical ML work: In ancient times people build a lot of nice structures, such as pyramids, cathedrals etc. By trial and error many rules of thumb were devised and they more or less worked. But it's safe to say things like earthquake-resistant skyscrapes and modern bridge cannot be build without deep theoretical insights into structural mechanics. These are highly optimized, intricate structures. The same is probably necessary to build highly optimized, intricate models that deliver what we now consider to be "AGI" - but then again, the world of ML is full of surprises.

Re: The Machine Learning Job Market

#175

Post by cwp »

minimaxir wrote: post #112
cwp wrote: post #104

Earlier quoted context omitted.

Nah, you wouldn't have to quit. If you've got 5 years experience, even on non-cutting edge projects, the PhD won't matter. Sure, you won't be able to get any job you want, but there are lots of ML jobs that list a PhD requirement that will nevertheless jump at the chance to hire someone with practical experience.

Hiring managers are more generous, but HR screening the resumes aren't.

That'll only be a problem if you're up against significant number of candidates that have both experience and a PhD. HR will certainly filter resumes based on something as clear-cut as a degree when they can. But they can't just filter everything out and tell the hiring manager he's SOL. So you don't have to check all the boxes as long as you check enough of them, and you're competitive with the other candidates.

At the same time, nobody is going to do all that well if they just apply online and cross their fingers. You need some kind of human contact, either though an introduction to an insider through your network, or through a recruiter of some kind. It takes a bit of time to develop the relationships, but it's quite doable and worth it, even for introverts. Best to start before you're interested in changing jobs.

Re: The Machine Learning Job Market

#176

Post by lysecret »

Not sure why this gets so much hate. I think he's a bit too optimistic about AGI prospects. But in his position these are all interesting and reasonable options.

I'm a bit sceptical on this 10,5,1 whatever year ahead metric he pulled from wherever.

Interesting read regardless. My opinion about the next few years is that most value will come from finding your niche, creating Datasets, iterating and building your ML model (a bit like he wrote but without this AGI...)

Re: The Machine Learning Job Market

#177

Post by pvalue005 »

tmp_anon_22 wrote: post #72
redredrobot wrote: post #55

Earlier quoted context omitted.

Currently on the job market in the AI space in the Bay Area - 400k to 600k is senior level at FAANG + similar. Low 7 figures at staff wouldn't shock me (although I don't have any actual data on that)

Can we pause and admire a data scientist drawing conclusions while being utterly unconcerned with underlying data? ;)

that's ok, they are bayesian

Re: The Machine Learning Job Market

#178

Post by pvalue005 »

me_me_mu_mu wrote: post #75
ozten wrote: post #69

Author is going to Halodi Robotics. I always thought that human shaped robots are a terrible form factor. Why limit yourself to the awkward design that 3.77 billion years of evolution accidentally landed on?

Why do you think it is an accident? I thought evolution is an adaptation mechanism. If anything I'd say we've got a pretty cool form factor (peak human form, not like me who is out of shape lmao).

The human body has been optimized for a very complex objective function, and in a very different environment to a robot. If you specify what the robots are doing, and the set of constraints like power source, size, weights, etc., the optimal design will unlikely be humanoid.

Re: The Machine Learning Job Market

#179

Post by warner25 »

xpe wrote: post #165
warner25 wrote: post #126

Can someone with only 6 years of experience make credible predictions about things 20 years in the future? I'm around 15 years of experience, and my appreciation for my own lack of knowledge and ability to make predictions still grows with every year.

I’ll bite on the above loaded question: Define experience. However you do, it should at least include work, education, and life in general, given that such experiences relate to the context or situation.

Yeah, it was a bit snarky, but still an honest question. In some domains, like astronomy or geology or climate change, I guess it seems reasonable to make predictions 100 or 1,000 years in the future based on available data rather than experience. In other domains, like politics or economics or finance, it seems like there would be much more value in having worked through a bunch of election and business cycles over the years. I'm not sure where computing and AI sits on that spectrum. I can imagine a young AI researcher failing to realize that some approach was tried and found to be a dead-end 30 years ago.

On the definition of experience, I agree that education and life experience counts. I said "around 15" years for myself because the definition is fuzzy. I got some very specific career preparation and training in college, so that sort of counts, and I probably spend more personal time than many of my peers learning about relevant history and current events.

Re: The Machine Learning Job Market

#180

Post by geebee »

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.

I've heard about this, in different contexts. What it mainly comes down to is that incremental improvements can have massive impacts when you can apply them at a scale available at FAANG. I first read about this outside the context of machine learning, but it certainly would apply here.

For those of us who don't work at such scale, can you (maybe with a little fuzziness to avoid telling too much about an internal project) give a few examples of the kind of projects where a fairly simple model can have a 1M+ impact?

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