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…
The Machine Learning Job Market
111–120 of 276 posts
Re: The Machine Learning Job Market
#112As a relatively unremarkable data scientist/machine learning engineer of about 5 years, I've been keeping an eye on DS/ML positions as they tend to give a sense on what is important to companies in that space, although I'm not actively looking for a new role. More and more positions seem to require Ph.D. credentials even for non-senior roles, even though modern DS/ML tooling doesn't require it. If I ever left my job…
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.
Re: The Machine Learning Job Market
#113Earlier quoted context omitted.
collectively? yeah, sure. this is absolutely probable in any organization of that valuation/marketcap, the most interesting thing here is just how fast crypto organizations can accrue and extract value. tech sector is fast, crypto subsector is like an order of magnitude faster. its similar to tech employment in the 90s where there was fast vesting (mostly due to quick exits), liquidity at super low valuations that th…
I'm just multiplying 1.6M by 1000. assuming a Solana engineer held on to every token they were granted.
Team and advisor allocations have been this, and have been my best trades. Vesting grants for employees can be lucrative too. Often times these are also discounted prices to whatever any buyer can get. So things amplify very quickly, and there are less ways to lose.
Re: The Machine Learning Job Market
#114I 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…
This is the answer. I have grown more in ~7 years* of random SFBA startups than I did in the previous 13 years of career in Europe. Just because the kind of startup that's a dime a dozen over here is a once in a lifetime opportunity back home.
To put this contrast into numbers: In 2021, during the pandemic while "SFBA is dying" was the mem, the Bay Area raised as much startup investment as all of Europe.
*I wasn't as career aggressive as I could've been, mostly for visa-related reasons.
Re: The Machine Learning Job Market
#115I 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…
Re: The Machine Learning Job Market
#116I 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…
1) The PhD takes a huge hit on your opportunity cost.
2008-2014 is 6 years of time; for me, it was the delta between starting my career as a junior engineer and becoming a tech lead at a hot unicorn which let me pivot to a CTO role at a small startup.
2) Academic credentialism has real effects.
This guy did a CS degree at an Ivy in the US. He has been set up for commercial success in the US tech industry through a halo effect you cannot also access unless you gained access to that institutional grooming at the same age. By choosing to do that PHD in EU (and a no name one at that), you forfeited that access.
In my experience, while the effect of this goes down over time, it has extremely strong launch + early compounding effects.
3) Risk tolerance can work to your benefit or against it.
You are working at a FAANG which is the safest and most cash lucrative option. In all likelihood, you have a great WLB and now a great blue chip brand on your resumé. However, the cost of this is that you're generally not going to get access to projects or culture that, by virtue of your participation, set you on an extremely steep growth path.
To get access to that, IMO, there's no real alternative to achieving strong outcomes working at a startup. Of course, that can be hard to do -- how do you figure out which ones are future winners, and how do you get them to let you come on board? I have no great answer rather than early career trial and error (accepting some of it will work out poorly and uncomfortably so).
I wouldn't say that "OP is a beast" per se, but it's much more likely that they have been groomed (working in the right conditions) in ways that you may not have. And yes, startups titles are not comparable to big company titles. It's apples and oranges.
The company he joined is a Series A startup, so absolutely an early stage company where whether you're VP/CXO, you're functionally going to be doing a player/coach role at most with tons of strategy baked in. But I wouldn't call that inflation, per sé. Sure, it's not the equivalent of being an experienced people leader and executive at a big corporation manning a giant organization at its helm. But you are often times in charge with significantly more responsibility and do not have bureaucratic friction and slow pace to hide behind. Doing a startup is just different. It's insanely risky, overall has poor risk adjusted rewards, and often is a magnet for shady characters. But if you can filter out the wheat from the chaff, you get access to the best career opportunities available, bar none.
Re: The Machine Learning Job Market
#117Re: The Machine Learning Job Market
#118Re: The Machine Learning Job Market
#119Earlier quoted context omitted.
As someone who is in a somewhat similar position as the author (looking for senior ML roles), I found this part enjoyable: > I’m not like one of those kids that gets into all the Ivy League schools at once and gets to pick whatever they want. Followed by "FAANG + similar" and a deluge of options. Also, I feel like their message is pretty liberal with using future projections and implying it to be the present. For ins…
Ironically, he graduated from Brown University, an Ivy League school.
Re: The Machine Learning Job Market
#120"FAANG+similar : Low 7 figures compensation (staff level), technological lead on compute (~10 yr)" I don't know where OP is getting these figures from, but I doubt that FAANGs offer 7-figure comps to Staff-level people. It's probably more in the higher 6-figure level (400K - 600K).