I'm a ML Engineer at FAANG and I have been passively looking for the last half year

Obviously, learning all popular tech stacks is not entirely realistic, but IMO when a SE or any engineer is looking for work, at least 1-2 months of researching and getting proficient with the full breadth of what's out there for your interest is going to pay off. Unless you are moving jobs to just do the exact same thing somewhere else (who wants that) you kind of have to take it on yourself to show up the the interview appearing like an expert in skills you hope to get to really grow comfortable with on the job. You may even need to exaggerate your experience. Ultimately, specialization should be more about how you think than what facts you know, but the hiring market won't see it that way, meaning you need to just get really good at faking it and not be afraid to back it up later after some late-night study sessions. The other half of this is that 'levels' don't always translate acros…

As a FAANG employee, working with ML, what do you want to get from other companies, besides more money? It's hard to have more chips, for example. You run less experiments, you have less throughput in an already computationally tight environment.

Some perspective from the other side of this situation: I was a hiring manager at a company that paid well but not FAANG level. We had an office near a couple FAANG offices. We had a lot of applications from people who wanted out of FAANG. Some problems we encountered when working with FAANG applicants and a few ex-FAANG hires: * Non-transferable knowledge: Many of them had a lot of experience working within the abstractions and structures of their FAANG company, but struggled to work with foundational concepts once removed from their FAANG infrastructure. Some of them spent years trying to rebuild copies of the FAANG tooling they were familiar with just to accomplish jobs that didn't really need it. * Experience mismatch with the current team. An extension of the above point: Some candidates were high level at FAANG but less experienced with day-to-day operations than our in-house Senio…

I think the assumption would be more that FAANG would throw wads of cash at non-FAANG candidates than the reverse. There's a few "common knowledge" things (assumptions, but often accurate) about recruiting from FAANG that would certainly explain the lack of "wads of cash": * Golden handcuffs are real and deliberate and in many ways rational: very few other companies have similar-sized wads of cash to throw OR similarly-scaled problems where FAANG-level salaries are likely to produce significant ROI * FAANG stacks tend to have a lot of amazing in-house niceties that can't be found elsewhere, so there's a certain "are they an expert or are they just an expert at using Google's internal stack"? Would they be productive out of the gate or would they need to rebuild all that stuff first? Everybody wants the prestige of telling their CEO or their investors that they poached people from FAANG b…

FAANG reputations are no longer evenly distributed. Even though most people's opinions of the company have changed, Google engineers are still widely considered to be high quality. Apple engineers also have a great reputation but it's very rare to see them on the market. Facebook/meta engineers tend to be above average but have a reputation for being aggressive corporate ladder climbers. But if I hazard a guess, if you're struggling to find work, you're at Amazon. Amazon's aggressive hire->pip pipeline has, at least in my experience, destroyed their reputation. I've worked with plenty of ex-Amazon engineers in the past few years and they were all notably below average in skill. This is not to say that you are (if you even work for Amazon), but Amazon is no longer the resume bump it once was, if anything quite the opposite. Talking about "FAANG" is not really relevant anymore since there …

Whats your recommendation for backend engineers that want to transition to ml engineers? If you contribute to open source (tensorflow, pytorch) does that help? What is your day to day like? What minimum skill set do you need to function as MLE? Does taking ML courses in coursera help? Best of luck to your job search, god speed

> In terms of salaries, most companies tend to top out around half of my current comp. While, I am not in ML or ever worker in FAANG, here in EU my experience is also the same recently. Infact, I’d say I am fairly underpaid given my experience & responsibilities but the latest openings are disaster in terms of comps and benefits. Also, unlike what my EU brethren will parrot, most opening cite “hybrid” & “flexible” but then they reluctantly share much late in the interview that yeah need to be in office at least 3-4 days a week, and we prefer in-person meeting because “collaboration”! Sadly, watching the wages going down(despite all the touting of inflation adjustment), EU is slowly catching up to US fever without catching up to the wage levels.

To note, staff position requirements are very high. One because they cost a lot, and two because they're looking for proven people. Going from senior to staff or principal is a huge leap. Personally I have no idea how people expect to get staff at 6 years of experience. I'd expect a minimum of 8-10. You just need experience there. Below that you better be a genius.