I really wish that sites like these would branch out to more employers beyond just the FAANGs because the number of jobs in AI/ML at these employers is just a small fraction of what we see in the larger industry. You can prove that to yourself by just looking at LinkedIn. I think if we had a truly representative sampling of all people working with "AI/ML" related titles, we'd see that the the true total comp distribution is much lower, and probably just slightly better than a typical software engineer's total comp with a similar amount of experience.
The reality is that FAANG-type companies are the only companies large enough to pay these salaries and with enough hires to keep it anonymous.
These sites are also heavy on selection bias. If someone arrives at this site with a $150K AI salary, they're not going to go to the trouble of submitting it and getting it accepted. Instead, they're going to start updating their resume and browsing job sites while they seek these higher salaries.
Just some anecdata, I’m in ml not in faang and make more than the top end of the survey in the OP.
If I were to give some guidance, I’d suggest skipping on faang as they top out where they top out, and go downstream to smaller public companies who are growing and where you can deliver real value. Companies like snapchat doubled in 1 year, square doubled in 1 year, etc etc. If you’re already joining a trillion dollar company you need to enter as a leader to earn the top end.
You’ll never see these stats because 1) you don’t gain anything from sharing and 2) it’s easy to find who you are at smaller companies.
I have this issue talking to people on HN all the time. Fundamentally it boils down to this: The people making mid six digits or higher don't want to share exactly how much they make because there is very little upside, and people vote based on their own perspective of "what's true" which slants towards average.
The other thing that I don't like is the money-worship around here. I don't want someone else to feel bad that they're "only" making $120k a year. I want more people to help their communities and get involved with our democracies to help craft better legislation to protect ourselves from an ever more technological world. It really, really doesn't matter what type of car you drive.
But I've noticed something over the years: The people that go out of their way to give back never hurt for money. Doesn't work both ways (I know plenty of wealthy people that are self-centred) but if you give back it's a happier, more fulfilled life and as a side bonus you get a lot of intros with great upside.
I'm curious to know how you got into ML? Did you go the degree route? PhD? I'm guessing that's where the top end of the salary range goes to but would be interested to know your personal path and if you have any advice on how to start.
PhD. From what I found, the most successful people are the ones that marry the technical with the business and consistently deliver value. So it’s not talk/hype, but real value and competitive advantage and ability to communicate and effectuate change.
Interesting, as per my comment below, this is something I tried and failed to do well. Would you consider getting in touch for a brief chat? You can reach me on mr475 [at] cantab [dot] net - thanks!
I don't know... Glassdoor's system seems to work pretty well for collecting this kind of data. Sure the number of reported salaries is maybe too small to say anything specific about individual companies, but in aggregate places like Glassdoor and LinkedIn could probably tell us with high certainty what the distribution looks like.
In a world where (A) compensation is typically mostly in the form of liquid equity grants and (B) compensation amounts change rapidly year-on-year, Glassdoor's system vastly understates compensation in the field.
Co-founder of Levels.fyi here - it's not so much that these sites solely support FAANG companies, it's just harder to get other people to add their salaries. We're working to encourage folks at other companies to contribute through dedicated company pages [1], etc. It's had a real impact and we've started to collect much more data across more companies / roles. We're also working to incentivize contributions more and I do think we'll be in a position to have a more representative sample over the next few months.
If I can provide some guidance, there is no upside to share your data when you’re at a small company because it’s really easy to find who you are. If there is a way to obscure the company, for example, generalize to market cap or size ($1-3b mkt cap company with 2500-5000 employees) and bucket that way.
I appreciate the transparency of the data collection. I go to Levels for my first look over the Glassdoor’s of the world because I can actually trust the provenance of the data.
I like this site. It's also depressing. I see that new hires at my company are making substantially more than I was at that level, even accounting for inflation.
If you’re on the seller of labor side of the transaction, you should be happy labor is selling for more. Typically if new hires are getting paid more, then experience people will be getting paid more too.
At my current company, we're experimenting with adding a market rate salary review component to our regular performance review cycle - we have a target salary percentile (based on years of experience, skillset, location, etc with aggregate market data from PayScale) for where we start new hires in a given role, and if for whatever reasons the market shifts upwards and leaves someone on staff below the new-hire percentile, we adjust upwards. We hope that regular pay increases, incentives, etc mean nobody's compensation ever falls lower than market rate after they're hired, but we also want to make sure we have a mechanism in place to correct if it does.
This is a feature we've been asked to add to our salary comparison tool (https://peerwyse.com). The request was to be able to create peer groups that you, as the manager, thought were appropriate peers for your report. Then you would be able to see the probability the report is in the different quintiles.
In the specific context the manager requesting this feature thought their reports were being underpaid and was looking for evidence that they could use to lobby for more.
In general that would be true. The problem for me is that it requires switching companies. I'm not able to do that since I naively did the jobs I was assigned. Now I'm a Neoxam and FileNet resource with zero prospects.
Seems that's the major flaw in self-reported salary aggregation sites: Because it's self-reported, you're generally going to get only one side of the bell curve: The side that people feel good about reporting. The FAANG employees, probably just the subset of them that are well paid. Who wants to go to a web site to report to the world about their utterly modest, average salary? How depressing! I wouldn't want to.
When I go to a site like levels.fyi looking to compare my comp, I'd have to believe that I'm not seeing "average" engineering salaries, but an average of the very best salaries at the very best companies. EDIT: Looks like you addressed my question in your comment already. Removing it!
I'm not convinced you're actually seeing the "very best salaries."
I've not posted my compensation at some firms because it'd be too easy to identify me. I'd say that levels.fyi often _underreports_ what levels of compensation are available to people of a given level.
We're trying to solve the salary sharing problem more generally with https://peerwyse.com by having you submit estimates of the salaries of your LinkedIn connections and then sharing back the aggregated view.
If you bring a few colleagues from the same locality / industry your estimates of mutual connections can be astonishingly insightful without anyone needing to break the taboo of revealing their own compensation information.
The goal of my project is to predict the salary range from job opportunities (and Linkedin profiles in the future) using Deep Learning method (and ad-hoc rules). My plan is to cover countries outside US as well.
Interesting, basically it is 4 companies, plus DeepMind, plus some small residual.
I remember looking more closely at this sector, from a UK perspective, and finding next to no well-paying jobs, never mind interesting (well-paid on the scale of this webpage). If there are some I’m dying to know!
But if this is a wider trend, it is odd how not just the internet, personal data etc that are owned by FAANGs, but the market in AI jobs too.
It would be nice to see the years of experience and education for the different positions. AI/ML is a rapidly growing field the past few years so a lot of these individuals probably came from academia or different industries.
Agreed, without the number of years of experience (there is possibly a loose correlation with the levels indicated) it is hard to make sense of the data
I only meant the top ones. There's a big discontinuity in the data and this mistake is more logical than seeing $3m equity grants that no other site can cross validate.
You don't get a sign-on bonus every year either. Agreed, I'd say at most you're getting $250k/year in equity, no way L6 is getting $3.2M in equity over 4 years
The sign-on bonuses are amortized over the equity vest period (which is a bizarre way to do it, but whatever). Aggressive L4 offers can clear about $250k/year in equity, so I don't know where you're getting this "at most" quantity...
Research scientist ladder at Google is a higher comp band than swe by, at least for lower levels, about 1 level. So an L6 RS getting high l7 swe comp isn't out of the question.
That said, the person getting such a large stock grant with a lower base salary is suspicious. The other two offers for L6 are clearly correct as they are (160K/year stock is too low for L6), but that one is odd. Doesn't mean it's wrong though.
For some context, initial grants are usually larger than refresh grants, and an L6 SWE (who again is in a lower band than L6 RS) can expect a >200K stock refresh. So a new hire RS at that level isn't going to get less than 200K annually (or 800K/4yr).
I've seen (and tried to compete with) mid-level (not quite senior, not quite entry-level) pure SWE (non-AI, infrastructure-type roles) offers with that much equity at public firms.
Are these salaries unique to research scientist in AI only, or do FAANGs pay these type of salaries for research scientist outside of AI in those same pay grades?
Unsure if this answers your question but a colleague of mine 2 years out of school is at a 12 person Robotics/Autonomous Vehicle startup based in SV making 160k base.
> Also, do FAANGs typically grant more equity after the 4 year vesting period?
Yes, they typically hand it out every year with their additional stock grants/refreshes.
When you start, you initially get a big dump of shares that vest over 4 years. Then every year, you get some more shares that vest over another 4 years. By the time your big initial stock grant is fully vested in 4 years, you already accumulated 4 years worth of yearly stock grants, that they pretty much replace and outnumber (in aggregate) that initial big stock grant, esp. since each year your yearly stock grant will probably be larger than the previous one.
Note: that's not the case at every single FAANG. For example, MSFT (which I know specifically isn't in the FAANG abbreviation, but is still often counted as a part of it; and I know that what I am describing below holds true for Amazon too, but they have it even harder, according to my friends working there) is specifically known for those yearly grants (that usually come at the annual rewards time) being too small. I can attest from my own experience, because all of my additional stock grants pooled together over the past 3 years accumulate to less than a half of my last vest of the initial big stock grant. Even if those 3 years of accumulated stock grants were to fully vest all at once on the same day. And, mind you, my situation isn't even that bad compared to the average, because I have been hitting the rewards performance metrics significantly above the 100% target, hovering around 180-200% every single year.
Yes. There is usually a base retention grant every year, which is roughly one quarter of a new hire grant, with a 4 year vest.
Then there are tiers of additional performance grants on top of the retention grant, which can be significant, perhaps as much or more as an entire new hire grant.
In a well-designed RSU grant system, employees basically end up on an RSU vesting treadmill that always has that carrot on a stick in front of them. The golden handcuffs to keep them around.
Specifics vary by company, but that's the template most start from.
They do grant enough to keep your comp in the similar range (but usually comp does go down). So general rule of thumb is to just switch every 4 years to get another grant :P
It's so odd to see a site like that in es, the top domain for Spain.
Do FAANG have offices in Spain? I don't think so. The site is totally out of touch with reality, misleading younger developers who see that and think oh great that's what I will aim to after I graduate.
A developer in Spain would be a lucky to have a job in the first place, for one tenth of that.
