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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 is a pretty big range of expectation for engineers coming out of each of those letters in the acronym. Additionally none of those companies command quite the attention they did a few years ago, as they've all shifted from demanding engineering skill to demanding the ability to survive in a large mega-corp.

As someone who has sometimes hired people from FAANG into startups I've worked in, I'd say it's 50/50 if the person will actually be a net positive to the organization. A lot of times the people coming from FAANG seem to be under the impression that what their previous company was doing is 100% the best, in all cases, and they try to cargo-cult their previous culture into the new company.

Sometimes they're able to adapt though and have a really positive impact overall. But the sour experiences really woke me up regarding seeing it as a mostly positive signal that someone worked at FAANG. So I wouldn't see FAANG as a guarantee about anything, no matter which company was their previous one.

The culture around FAANGs has always been a bit strange. People associate making a lot of money with being good at building tech at both a personal and a company level and unfortunately that isn't necessarily valid in either case.

New hires who have been living inside the Big Tech bubble for a while can come with issues that others here have already described. Consequently startups should probably downlevel many people coming from FAANG roles for exactly the same reason that FAANGs downlevel many people who haven't been worshipping at the altar of scalability in their previous roles. The skills and methods are different and some of them aren't very transferrable.

Of course that's not to say good people who have been working at large scales can't or won't adapt. But on day one nobody cares that you've been a Staff/Principal leading projects with 25 developers and a mature development environment if you're joining a startup that needs to raise a Series A before the end of its runway this summer. The abilities you need in that environment are things like identifying the most essential supporting work that can't wait while still spending most of your time on getting the main job done and hitting the deadline. If you can't roll up your sleeves and get the job done then you don't need to worry about a PIP. There are only 10 of you anyway and you're all out of a job in six months if any of you doesn't pull their weight.

It would be absurd to downlevel these folks. Probably the mismatch is an investment and both org and employee will need adjustments that are mutually beneficial.

It would be absurd to downlevel these folks.

In which direction? Both make sense to me. A good employee might adapt to their new environment quickly and in that case obviously they should then be levelled up appropriately. But there is nothing about being Staff/Principal at a BigCo that says you're going to be anything special as a senior in a startup - in fact the opposite can be the case for reasons others have mentioned - and there is nothing about being an excellent contributor at a startup that says you're going to navigate the scale and the politics of a BigCo effectively. It seems reasonable to start a new hire at a level where you would expect they do have the required skills (including soft skills) and then look to make more effective use of them if and when they've settled into the new environment.

I'm not sure what part of my comment made you think that my current employer is an obstacle in job hunting. Actually it's quite the opposite, as I mention I get a lot of attention from recruiters even though I'm not actively looking. The issue I'm observing is that this attention doesn't clearly translate to more job opportunities.

If my current employer was the problem, wouldn't recruiters and hiring managers not bother reaching out, or reject my applications (I haven't applied to any job directly recently so I can't say).

I'm speaking to your point about:

> Common wisdom might make you think that I should be having companies throwing wads of cash at me

My point is just that you're misunderstanding "common wisdom". In 2016, yes a job at any FAANG would mean that you have no trouble getting a new gig. In 2024 Amazon employees do not have a great reputation, so there's no reason to think of yourself as part of a group that "common wisdom" would show preference towards. Nobody with "wads of cash" sees Amazon on a resume and thinks it's something special and worth reaching out for. FAANG in general is not as appealing as it once was, and Amazon in particular is not a strong signal at all. Someone working at OpenAI today would likely have the response you're imagining is reserved for FAANG.

Recruiters are also playing a numbers game, especially in this tech job market. My inbox has ample recruiter message for roles that are not even relevant to my skills, I wouldn't mistake that for signal.

Amazon, like any other big company, is a 1000 different small companies in a trenchcoat.

Same for the other FAANGs. Each has just as least as many meh engineers as it has great ones.

I think they were saying that because they're an experienced ML engineer more than because of what companies they've worked at.

I kinda agree that Amazon is no longer a resume bump. But otoh, if a company holds a lower expectation on an ex-Amazon candidate (compared with average - "quite the opposite" as you said) just because the pip thing exists, I'd say that's very short-sighted and irrational, and would probably think again if I really want to work for them. Similarly, if they automatically think that an ex-Googler is worth hiring without proper evaluation.

"Reputations" of companies you worked for should have the absolute minimum influence on any hiring decisions. Don't blame others if you don't have a proper hiring process that works for you.

Yup, this is true. At my workplace, during the hiring frenzy of 2020-22, we had an unwritten moratorium on Amazon. SDEs were interviewed only if we didn't have anyone left. SDMs, only if it was an internal referral that personally vouched for them.

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 but few have the wallet or the precise scenarios to get into a bidding war about it.

I had these issues as a HM in 2020 trying to hire ML engineers. (At mid-size places there's also often a "WTF do we do with a ML engineer" problem where if you aren't a data scientist or a general SW engineer, it's hard to carve out a role in between. Especially if the org has data scientists who aren't particular "engineer savvy" in terms of workflow and tooling. And then the ML engineer makes things shiny and version-controlled and all... and the data team mostly just ignores it since moving over to that stuff isn't one of their KPIs...)

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 across environments. Just find work that resonates with you, and you will move up. Obsessing over the level (or even the pay) is probably limiting.

> You may even need to exaggerate your experience. [...] 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.

Please don't do this, and don't normalize other people doing it.

Dishonesty isn't something I want to have to be understanding about in colleagues. Like, maybe they just see it as a little while lie, and some job-hunting Web pages told them is ordinary to do, and you'd be at a disadvantage if you didn't do it, and you're even to be commended for your scrappy can-do resourcefulness in fudging. I'm ready to be understanding about many situations, but I want dishonesty to be something we can summarily nuke from orbit.

Also, when I'm in an interview to possibly join a company, I don't want there to be more rationalization for crappy interview process. ("I see from your resume that you have decades of experience, including sole author of open source packages, but you could be lying about all of that, you dirtbag, so let's derail this meeting by focusing all our energy on some BS test that's been gamed to heck.")

> Please don't do this, and don't normalize other people doing it

Unfortunately, that's how the competitive market works.

People shouldn't be penalized for the tech stack that their company used vs. what companies are looking for. Skills from one platform to another are very fungible (eg - Java/.NET, AWS/Azure), so "beefing up" a resume is what's required if you plan on getting any calls back.

> Dishonesty isn't something I want to have to be understanding about in colleagues.

I have some bad news for you...

> .. I don't want there to be more rationalization for crappy interview process.

No one is forcing you to participate in interviews which require doing a coding test or system design review. You are more than capable of withdrawing your candidacy.

I would bet that a lot of equally-skilled people are lining up behind you and ready to do that extra work in order to stand out and get a job. Don't place yourself on too-high of a pedestal, my friend.

> Unfortunately, that's how the competitive market works.

That's how some people think the competitive market works. It's not universal.

Which is why I just contradicted that, and called to not encourage those beliefs.

Put loosely, half the job of an engineer is to tell the truth.

What I think you're characterizing is a corporate bullshit shoveler who just wants to go through the bullshit motions to get a paycheck.

I really don't want some impressionable junior engineers to see multiple people on HN encouraging lying.

I do not want to wade through that, to separate the merely misled junior engineers, from the hopeless anti-engineers.

> You are more than capable of withdrawing your candidacy.

And I do. But I'd prefer that the situation improve.

I am pretty tolerant when it comes to not knowing things or time for learning new skills. With this fast changing industry i can not help it but tolerating.

But when i catch someone lying at least twice about something technical, i do my part to make sure the person is not hired or next to leave. There is no trust.

An engineer who can’t admit they don’t know something when it matters is actively harmful to the work of engineering.

This. I am genuinely excited when I am faced with a problem set where I don’t know something (e.g. need to research, explore, gain a skill, or otherwise educate myself) — seriously, it’s the best. The only thing better is learning (or being told) that I was wrong about something and then re-evaluating my belief/understanding stack in order to find and correct the error. I have such little patience for folks fronting or faking knowledge/expertise - it’s such a fucking waste of time for everyone else.

> An engineer who can’t admit they don’t know something when it matters is...

a bad engineer.

I was really amazed about how many people lie on the resumes. E.g. a recent study [0] said 70%, but there are many of these with similar numbers. There is a difference between putting your best foot forward and just straight lying for financial gain, which is fraud, and can (very rarely) send you to jail.

Personally, if I suspect dishonesty, it's a strong no-hire. If I even get a whiff of dishonesty, I'll at least flag to recruiters, hiring managers, and interviewers, and we've actually compared notes and realized a pattern of behavior before.

It is hard though since a lot of the stuff they would lie about are things you can't verify.

0: https://www.forbes.com/sites/bryanrobinson/2023/11/05/70-of-...

While I agree with you on the principle, I would not blame anyone who lies in such a BS market to be honest.

If for a person, the choice is between not being able to pay rent because they had 6 years of experience in a given stack instead of 8, vs. exaggerating in their cv, I dont think it is reasonable to expect complete honesty.

Also we all know most of the companies are often very dishonest, so why should we expect complete honesty from the workers?

> 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.

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.

It sounds like a Staff title may be in play, although it wasn't made clear whether this would be a promotion.

The traditional answer is stability. I think that's still true despite the sense of looming layoffs. Everyone always knew the layoffs would come one day anyway...

Fully remote would be a pretty nice perk, ever since our companies forced us back to the office. I believe this alone might be one of the easiest ways for smaller companies to poach talent these days.

Other than that, I enjoy working in hard and impactful problems. I'm privileged that I was able to do that in FAANG, but it's unclear how long this will last. I know that is very trite to say that your company has changed, but I truly feel it. Our company missed the LLM train and we are scrambling to play catch up now. Also we mostly pivoted from bold and inspiring initiatives to efficiency and cost cutting. Most of the initiatives in our pipeline for this year are pretty dull "let's save some dollars here and there". Nothing wrong with that, but given the choice, the chance of working on a moonshot vs helping a trillion $ company save pennies might convince one to change jobs.

There is also the very trite but very true aspect of "small cog in a huge machine". I might engineer a new system that brings in tens of millions of dollars to the company, and I'd be lucky if 3 layers of management above me knows about it. Those kind of initiatives might influence the entire company in a smaller organization.

All in I recognize I'm quite privileged and doing pretty well in my current job, and that's why I'm passively looking. TBH one of the aspects is that I got FOMO from all the talk about ML engineers being in high demand. However my experience so far has been very underwhelming.

Where have you heard that ML engineers are in high demand? I don’t think they are immune from the tech slowdown that’s affecting the industry. Of course, LLMs and AI are all the rage, but the demand for that is in the services/APIs not necessarily the demand for ML engineers

I suspect we'll see a broad slow down in classic ML approaches. If I just need a basic classifier -> GPT-4 can do an OK job. Spending an afternoon prompt tuning is vastly cheaper than a dedicated team of scientists and engineers curating datasets and running experiments. For many SMBs, this effectively eliminates the ML function.

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 Senior/Staff devs. This put us in an awkward spot where we'd have to pay them more than our current high level engineers to bring them in, but they'd be operating at a level below them at least to start. We opted to promote from within in these cases.

* Compensation cold feet. Many would tell us they just wanted out of their FAANG co at any cost and knew it would come with a compensation cut. But when it came down to offer time, they'd get cold feet about leaving money on the table. Even in cases where we paid within ~$50K of their FAANG comp, some of them just couldn't let it go when it came time to put in their 2 weeks' notice.

* Boomerang FAANG employees. Some people would join us, then miss the structure of blending into a big, highly structured FAANG company. They'd bounce back to the same FAANG or another FAANG within 6-18 months.

* Higher rate of candidate ghosting us than with any other cohort. It was rare for an average candidate to ghost us once the interview pipeline had started, but for whatever reason the FAANG applicants had a very high rate of getting half way through the pipeline and then disappearing without a word. Many times they'd appear again a few months later asking for a second chance, only to repeat the process.

I'm not suggesting these apply to you. Only trying to provide some perspective. Anything you can do to alleviate these concerns early might help an application process.

Of course, if your goal is to secure FAANG level compensation and Staff+ titles, you might just have to accept the reality that continuing in FAANG is far and away the easiest way to get there.

Thank your for sharing, it's great to hear different perspectives on the topic.

Non-transferable skills is actually one of my biggest concerns about working at the same company long term. These tools are often so far from the industry standard that it can hurt your employability long term as you mention.

It's no secret that devs often interview with companies they don't intend to join just as a warm up exercise. I confess I've been guilty of this.

The boomerang is quite interesting. Up until very recently this was a very well known career move. People would leave the company for one year just to come back at a higher level and higher salary. As a "loyal" employee I find this furious, but alas I guess it's my fault for working for such a lousy company and not pulling the same move. If it is any consolation, at least our company change the rules so that it's harder to pull this off, so you should see this much less often these days.

Regarding your last comment I'm not particular about leaving FAANG, as I aren't about working for FAANG. Considering my priorities it's very likely I'd only take a competing offer from another top tech company. For now I'm comfortable enough staying in my current company and passively looking for a better opportunity. At the same time I want to avoid becoming too complacent, and I'm aware the rug might get pulled from under my feet at any moment (hello layoffs?).

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.

I think if you want a "staff" level role, you have to have been doing this since before there was such a thing as a "staff" level role.

I never heard of people aiming at Staff+ positions with 6 yoe, but surely sounds like a stretch. I'd say that 5 years from new grad to senior at big tech is a fast-ish progression; a bit quick, but doable with skills, hard work and some luck.

I personally have +15 yoe total and I'm currently Staff in my company.

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

That's a lot of questions so I'll try to answer one by one.

> Whats your recommendation for backend engineers that want to transition to ml engineers?

I get this a lot, and my first question would be, why do you want to work in ML? Is it because it's "cool"? because it's in high demand? because it pays a lot? for the second two points, see my comment. For the first point, I'm pretty cynical about it. At the end of the day it's a dev job not very different from other domains. Depending on where you end up, your day to day might look the same, but with focus in ML systems.

Nevertheless, if you really want to transition, I still think the easiest way is to do an internal transfer to a team that does ML. That's how I got this job. If you are a good engineer, most ML teams will be happy to have you and teach you the skills on the job.

Of course maybe there is no ML team in your current company. If you are in a small company you might try to influence leadership to start ML initiatives and get some experience. I've seen many candidates do this: they start an ML project in their current company, and after 6-12 months they look for a job elsewhere. This is a variant of CV driven development, and sadly often these initiatives are hamfisted and don't really solve business problems.

Otherwise you might try to get a job in ML in another company. Lucky for you I found some companies that hire engineers in ML or ML adjacent jobs using a standard software dev loop, i.e. no special ML round, although these difficult to find. The other option is to try to learn enough ML to pass an interview.

I won't lie, this is doable, but the deck is stacked against you. The job market is pretty insane right now, and you'll be competing with people with masters and PhD's in ML, plus engineers with years of experience in the field.

I believe it's fair to say that pure theoretical knowledge won't get a job, much less unless you have prestigious credentials. Instead I would focus on a blend of applied theory and personal projects. Probably it would be best to choose one ML framework and double down on it to increase your chance of finding a job that requires that particular framework.

For theory there are tons of courses and resources online, although I tend to like more walkthroughs and tutorials that guide you on building a system rather than pure theory. For practical experience, the sky is the limit. As I said there are tutorials online that guide you on a project, but that will almost certainly not be enough. You can try your hand at Kaggle competitions, and at some point you can just choose some personal project that interests you and try to build on it.

I hope any of this helps and best of luck to you. Feel free to DM me if you want to know more.

People are no longer looking for ML , they’re looking for LLM.