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The Machine Learning Job Market

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Re: The Machine Learning Job Market

#221

Post by tharkun__ »

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.

As a hiring manager I have learned that some people are trying to be deceptive that way and can thus no longer assume.

Thus I have to make sure either way and probe a lot unfortunately. Did they hold the title for the last 2 months and are jumping soon after? Will they do the same here? I want to know about and see the progression. There are situations where it is sort of irrelevant but in others it is detrimental if I have to probe.

If you are say in year 5 of your career and at senior level at just one company I want to know if you were a junior when hired out of college, were super awesome and made intermediate after one year and have been senior since year 2.5. You can show that to me right on the CV by listing it individually. If you just put the end title and that's it I will assume that you made senior this week and are trying to jump ship. This doesn't mean we can't figure it out together in the interview if it gets to that stage. But it sets a certain tone and connotation for the entire conversation. A bias to overcome.

Re: The Machine Learning Job Market

#222

Post by oneoff786 »

geebee wrote: post #180
oneoff786 wrote: post #17

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.

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 pro…

I’m a consultant. I actually do a very different kind of thing. Yeah big tech hyper optimized content so that a tiny boost of engagement improves a billion users by a tiny amount so the net effect is huge. I’m skeptical of it tbh. I think it forgets emergent effects and externalities over time.

What I refer to for my work is the low hanging fruit. Old problems that businesses solve with manpower or overly unspecific rules. Something where just a little clarity can help them hone their efforts on the 80/20 of it all. I made a slightly more detailed post in an adjacent response

Re: The Machine Learning Job Market

#223

Post by mediumiqsad »

toddm wrote: post #157
ZephyrBlu wrote: post #119

Earlier quoted context omitted.

And he worked in one of the most exclusive teams, Google Brain.

Kids who graduate from Ivy League schools get a lot of job offers at once and get to pick whatever they want.

No post body was provided.

Re: The Machine Learning Job Market

#224

Post by mediumiqsad »

nrmitchi wrote: post #50
Havoc wrote: post #22

Interesting that Tesla gets its own row in the pro/con table while the faangs get lumped together.

Why is this interesting? Tesla is... not a FAANG company? Not only is there no 'T' in FAANG, but the industry/product is completely different. Or maybe the author just wanted to make a joke about coffee. Who knows.

No post body was provided.

Re: The Machine Learning Job Market

#225

Post by barry-cotter »

21723 wrote: post #192
going_ham wrote: post #71

This post feels like author is insecure about his position and wants to establish some validity. Having going through it all, it feels delusional at best. The glorified pattern matching can only take us so far. You know it's working as long as there is a pattern. I wouldn't call it a general intelligence per se. There is no "juice" in these algorithms. If we use these tools, we can immediately see where they fail and…

AGI is very unlikely to happen within the next 50 years. Dangerous limited AI exists now and it's going to get worse. I don't worry about malevolent AI, because we don't even know what consciousness is, nor the limits of a (presumably) nonconscious entity's attempts to emulate intelligence. I worry quite a lot about malevolent humans using enhanced technology (note that most things in technology, once accomplished, c…

> I don't worry about malevolent AI, because we don't even know what consciousness is, nor the limits of a (presumably) nonconscious entity's attempts to emulate intelligence.

It doesn’t need to be malevolent. You’re made of atoms and if the AI has uses for those atoms goodbye you. There’s no reason to believe consciousness has any impact on the ability to maximize an objective function, i.e. try for a goal.

Re: The Machine Learning Job Market

#226

Post by nothrowaways »

21723 wrote: post #194
mysterEFrank wrote: post #146

Earlier quoted context omitted.

Eric Jang is top ML talent, these numbers are accurate. I work in ML and have followed his work for years

If he's actual top talent as opposed to a poseur who's good at self-promotion, he should stay in academia for his own sake because he'll be crushed in the corporate world. Actual high IQ people get clobbered in corporate, while OKR-ing charlatans climb the ranks effortlessly... yes, even at FAANGs.

Academia will be difficult for him as he does not have a PhD.

Re: The Machine Learning Job Market

#227

Post by falsenine »

rakejake wrote: post #153
falsenine wrote: post #135

As someone whose intention is to go to Medical School and pick up programming (+ math skills) to potentially work at the intersection of ML + Healthcare, the knowledge of the regulatory hurdles expressed is discouraging. Not sure if it really is worth the effort to study tech on top of medicine. Are there any people with experience within ML + Healthcare/Medicine or know of startups that are making great strides with…

I used to work in the healthcare vertical. While there are regulatory hurdles, they are there for good reason. Move fast and break things does not work in this industry and will get you fired. You will have better luck working with one of the larger companies who have a good history with the FDA, and more importantly, have good relationships with hospitals and physicians. They are aware of the time and resources it t…

Thanks for the comment. Do you have any feedback for a future physician interested within the intersection of ML + medicine (does not strictly need to be "healthcare")? Would learning ML and all the other things needed to know be helpful (huge time investment needed here)? Ultimately, would be interested in leveraging medical knowledge into a startup capacity.

Re: The Machine Learning Job Market

#228

Post by bricemo »

hintymad wrote: post #149
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…

I think this reasoning is flawed. Joining Google does not mean you get to solve interesting problems because you will most likely contribute incrementally to a vast body of work. You want to build the next-gen database? You have to come up with something way better than BigTable and Spanner. You want to build a queue service? You've got to come up with something way better than Google's PubSub, which optimizes itself…

It’s definitely a tradeoff. I personally have gone through alternate phases where I wanted to be responsible for a huge piece of a small pie (startup) versus a tiny tiny piece of a huge pie (big company). There are pros and cons to both. The big slice of small pie feels cooler and more satisfying to my ego, but in the absolute sense the tiny piece of the big pie probably is actually making a bigger difference. 1% improvement at some of these companies means north of billions of dollars and improving hundreds of millions of peoples lives.

Re: The Machine Learning Job Market

#229

Post by jaimex2 »

21723 wrote: post #194
mysterEFrank wrote: post #146

Earlier quoted context omitted.

Eric Jang is top ML talent, these numbers are accurate. I work in ML and have followed his work for years

If he's actual top talent as opposed to a poseur who's good at self-promotion, he should stay in academia for his own sake because he'll be crushed in the corporate world. Actual high IQ people get clobbered in corporate, while OKR-ing charlatans climb the ranks effortlessly... yes, even at FAANGs.

First I've heard of him tbh.

I'm not aware of anything he's accomplished but can see the delusion. ML people seem to think the output of their work is not mediocre. Yeah, you bred monkeys till something resembling shakespeare appeared to some reproducible consistency and it is better than something someone can code - but that's an incredibly low bar.

Acknowledge that were still very much in the stone age of AI and what were doing is large scale analytics at best.

Re: The Machine Learning Job Market

#230

Post by savant_penguin »

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.

Would you kindly tell what types of projects are such nice successes?
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