It was data. The "I must be doing something wrong" was tongue in cheek. The specific data point was that I am not seeing any salaries anywhere near that. And Salary.com actually backs me up.
Just the fact "grind Leet Code" is a recommendation to someone who's most recent job title was CTO and has written two books may be a sign these interviews are broken.
But point taken, I'm sure if I studied specifically for interviews I could get those jobs. But the fact remains, a vast majority of people don't work for FAANG and even the "person unwilling to work for FAANG" example is higher salary than I have ever seen.
My point was actually that the article and ones like this seem well research but don't match the reality. For example, I recently interviewed at a fortune 100 company for a Director position and it was what this article says is "non-FAANG staff engineer" salary.
Fortune 100 companies don't necessarily pay well for software engineers. The bigger a company is, the more that resources concentrate to the top of the company. Besides, a non-tech company will consider their tech departments cost center, which means the pay will not be good. Tech companies pay much better, both in cash and in equity. Note a company does not have to be technical in your eyes, but needs to be technical in its management's perspective.
> We always show salary as a normal distribution but maybe it is bimodal.
Who are we? Which salaries?
Starting salaries for lawyers are one of the most famous examples of a bimodal distribution you can find. Salaries across the country generally are less bimodal than that. But salaries shouldn't be modeled as a normal distribution in any event. They skew high very easily, and they can never skew low.
> Just the fact "grind Leet Code" is a recommendation to someone who's most recent job title was CTO and has written two books may be a sign these interviews are broken.
Well, I'm sorry you have to go through the same ropes that we peasants do, you highness.
If I was hiring for a tech position and had to choose between an engineer with a few years of experience and strong analytical skills proven through coding & system design interviews, and a person who prides themselves on 10 years as a CTO at a company I've never heard of and two book authorships, I'd strongly lean towards the former.
I mean “tech position” is extremely vague here if you’re going to make them code because what you actually need is an engineer then yeah I would prefer the leetcoder too but if you’re going to build a team and put processes in place then I’d go for the CTO. I think it all depends on the position specifics. Generally speaking tho the CTO interview is VERY different than a typical engineer or senior engineer.
The leet code thing is overstated. I’ve rarely seen “hard” questions asked and when they are asked, often the maximally efficient solution is not required to do well in the interview.
I'm a bit confused about what you're saying. levels.fyi is accurate and there are plenty of remote jobs available. So the 300k+ salaries are certainly available from Boston. If you're not willing to prove that you can pass system design, coding, and leadership interviews then that's a different matter. FWIW I recently interviewed for staff at a bay area big tech company and there was no leetcode-style coding interview: system design, leadership, presenting previous work, and a simple non-algorithmic coding discussion.
The comment I'm replying to specifically mentions Leet Code (I am assuming that is what LC stood for) which is the only reason I brought it up. Your point is exactly my point, leet code is not relevant to the type of position I'd be looking for.
So assuming Levels.fyi is accurate (their numbers are $20k more than salary.com which is more accurate in my opinion): my point was that I am average, right in the middle. But this article is telling me (and by extension, since I am at the mean, 50% of software engineers) are extremely underpaid.
And maybe on a nation wide scale that includes non-Boston metros, I am. But for Boston... I'm not over or under. I'm right in the middle.
Basically based on my research I think this article is for a very niche group at the top 25% of pay ranges and is not accurate or relevant to 75%+ of software developers (numbers are ballpark).
> leet code is not relevant to the type of position I'd be looking for
Leetcode is relevant to getting the money though. It may be the case that inexperienced new hires with Leetcode proficiency make significantly more money than CTOs in the positions you're considering.
> Your point is exactly my point, leet code is not relevant to the type of position I'd be looking for.
But the jobs you'd get with leetcode would pay twice your current expected liquid compensation at least (assuming 0 stock growth) for your experience level. If you don't want those then fine, but then you can't complain about threads like this either.
Google has jobs in Boston, I bet there are many other tech companies paying similar as well. Pay at these varies by location, but not more than around 10% within USA, so it isn't like it is your locations fault, if you worked at these companies you'd be earning very close to the same you'd earn in silicon valley.
I'm not complaining, if you read my posts am saying the article is not representative of a majority of software engineers' experience. Using my career as an example. I even explicitly say that in the very comment you are replying to.
Not everyone works for FAANG.
Also, leet code is not relevant because the questions asked at that level are typically not algorithms. I passed the leet code stage in my career 10 years ago. Questions I get asked are typically around architecture, distributed systems, scaling, etc.
So what you are saying is someone who can answer the leet code questions easily (and has been able to for a decade) AND is an expert architect, should get paid less than someone who can only do leet code?
> leet code is not relevant because the questions asked at that level are typically not algorithms
Top companies still ask algorithms at these levels and fail people at these levels if they don't perform at least as well as juniors. If you are L6 at Google you are looking at liquid compensation around $500k a year, and they still require mostly leetcode questions. Of course you wont get considered for those positions without a ton of experience, but to get them you have to do your algorithm homework.
> should get paid less
Not should, will. I didn't make any claims to what things should look like, I am talking about what the market looks like now. Your ability to do algorithms is more important for your compensation than your ability as an architect or as a leader except for extreme cases where you start to get into higher management positions at big companies.
Edit: And if you are great at algorithms as you say, but doesn't leverage that skill to get higher pay then that has the same effect on your compensation as not knowing algorithms. It is simple. So your insistence on only looking for jobs that doesn't test algorithms is probably the reason your salary didn't balloon as you'd expect.
I've never gone through a FAANG interview but I have gone through some fortune 100s and never once been asked an algorithm questions. But I have no reason not to believe you about FAANG.
Saying questions asked at that level are not about algorithms was based on my non-FAANG experience. I shouldn't have been so absolute.
But with that said, to back up and restate where I was trying to get. What I was trying to say is that algorithms are table stakes, you should absolutely know them at a senior level but the actually questions you should be being asked should be more advanced than that.
Big difference between FAANG and other fortune companies is that FAANG expect their managers up to very senior level to be technical and be able to code even if they do not code day to day. Other Fortune 100 companies are happy to have non-technical managers to manage engineers with mixed results.
In all SV companies coding interviews are strictly required at interviews at M1/M2 level (up to 60-80 reports) and required at some companies at D1 level (150 reports). CTO at start up with 20-30 reports is M1 and will have to do coding 100% and failing coding interview is automatic no hire.
I am not the GP and I mean no disrespect to you. Your below phrasing seems presumptuous to me -
>>..someone who can answer the leet code questions easily (and has been able to for a decade)...
If you are a CTO chances are you have not been practicing LC. Even if you were able to do LC style questions a decade ago, without practice you are very likely to struggle with them. So don't assume just because you are CTO / architect you can do LC style questions. The questions have been becoming progressively harder each year. It is a sad reality, our industry does not decide compensation based on experience, knowledge and actual skill used on the job. If you want to maximize your compensation you have to do LC style questions. Even non FAANG companies ask LC style questions these days.
I actually agree with you. You have a point. I would probably practice Leet Code or similar before I went back into the interview world just to freshen up.
Though, with that said, I have been on the interviewer side a lot and need to know some of them well enough to know if the candidate knows their stuff. So I'm not completely rusty. And as CTO I would often read through what other companies are asking in interviews to make sure what I'm asking is in line.
My objection was the implication that not crunching Leet Code problems was the sole reason for the low compensation.
But you are right. I should not assume because at one point I knew all those algorithms that I can still explain them under pressure of an interview.
Maybe you didn't mean to use that word but it's not about "explaining" them: you have to be able to write the code to solve a given problem, on the spot. The problem might require a familiar technique such as breadth-first search but it will need to be adapted; the problem is likely to be slightly different from anything you've seen before.
It's true that at the staff interviews i just did, LC was not required but at the staff interviews i'm about to do they will be.
I meant explain. I see explain as a superset of implement. I have met lots of people who can implement but not explain. I didn't mean a simple explanation, I mean actually going into depth about things like algorithmic complexity, memory usage, etc.
As an interviewer (granted not at FAANG) I see no value in you just proving you did an algorithm on Leet Code. You could just be passing it through rote learning. I want to make sure you understand it. Otherwise I'm just wasting your time. But that's just my interview style, I know bigger companies may be different.
> I mean actually going into depth about things like algorithmic complexity, memory usage
OK, I agree that's definitely something the candidate should be able to do. I'm just trying to point out to anyone reading that it won't be sufficient to explain an algorithm. You have to actually write code to solve a given problem. That's not the same as regurgitating code you remember from leetcode, so please don't think that. You have to apply techniques that you've learned. It's not easy, and it's not intellectually shallow.
>Just the fact "grind Leet Code" is a recommendation to someone who's most recent job title was CTO and has written two books may be a sign these interviews are broken.
This comment and those further down (particularly doubting the data) suggest that you're experiencing strong cognitive dissonance. There are, I'm sure, hundreds of engineers on HN that can testify to exactly how much FAANG pays and in which metro and how little experience they had (not including LC) before they started.
The claim that hiring is broken has been constantly repeated since ~2010 and yet all of these companies somehow, despite hiring all of the wrong non-CTO people, are still alive and are in fact thriving. Like I said: given how consistently these mantras are chanted I'm just curious what you're expecting to have changed since the last time a post like this was made (you did say "every time I see..." so you've certainly seen many). It's a little like that episode of parks and rec with the reasonabilists where they keep predicting and amending the date of the end of the world.
Don't take this the wrong way, but, having helped several people through the interview loop at FAANG, my experience is that it is frequently the ones that see themselves as above grinding LC, that have the hardest time making it all the way through. And I don't mean wrt wrestling with how tricky some LC problems are (most everyone struggles with eg DP) but with marshalling the discipline and patience to actually do enough to do well during the interview.
It has been said before but my personal experience reflects this: it is of absolutely no surprise to absolutely anyone that those that can grind LC fare much better once on the inside and facing the sheer abyss of complexity of FAANG systems.
Read what I said again. My point was not that algorithms are irrelevant but they are table stakes. I have known all the algorithms on Leet Code for years.
My comment was not that I don't need to know algorithms it was that at a senior level the questions should be far more advanced than what leet code has.
I'm a little amazed that so many people in these comments assume I don't know algorithms.
>My comment was not that I don't need to know algorithms it was that at a senior level the questions should be far more advanced than what leet code has.
Seniors (>=L5) go through a "pirate" round that's system design/arch in addition to the "ninja" round. But you're a CTO so you should be able to ace that (after handily passing ninja since you know all the algorithms). So what exactly are you waiting for? I mean like do you want a referral? I'd be glad to make a quick 5k for referring you since you're so confident that you're good enough.
Mostly because I stayed at my last job way too long and when I was finally ready to leave I started my own company vs job hunting.
I probably will start applying if my new company doesn't make it.
Though a lot of me wonders why I am working for no pay at my own company when it sounds like I can make enough to retire being an individual contributor. I do believe in my idea though and want to see it through.
I have friends at just about every one of the FAANG companies so a refferal's not the issue.
Edit: When I say way too long... I mean 10+ years.
Edit 2 because I just realized how it could have sounded:
I didn't start a company then give myself the CTO title. I was CTO at my previous job (the one I was at 10+ years).
If you are great at algorithms, have 20 years of experience with a great track record and live near a great tech university so plenty of tech companies, then you are doing something really wrong if you just earn $175k a year. Or you intentionally choose to have a low salary and prioritize other things, but if you did that then you'd know about it instead of asking this question.
You're being downvoted (not by me) but you have a point. I did two things wrong that I know of:
- I graduated college right before the great recession and that effected my career starting rates
- I stayed at a company I knew was under paying me for far too long (because I had a lot of stock options and I valued them too highly)
If either of those two things were different I'd probably be making a lot more. But my point was, regardless, I am making the mean salary for my region and that mean is not what is represented in this article.
I feel like those 2 points are just excuses(no offense but plenty of people would kill for a chance to even go to college)...if you were to go back would you hold off graduating college until after the recession? It sounds like you are extremely proficient in the technical space, are you good at negotiating and/or identifying where the value is? There is really no reason for someone to stay on a linear pay progression when moving from one job to the next, or even one title to the next.
A lot of research has been done on how the recession stunted career growth for anyone who graduated into it. Effectively anyone who graduated during it got passed over for the people who did graduated right after it. Leaving the people who graduated during it in underemployment and unable to move on because companies were more willing to hire people who were fresh graduates than people who were underemployed.
So yes, actually... in hind sight I should have gotten my Masters degree instead of entering the workforce.
Edit: That job I stayed at too long... only reason I took it in the first place was because the recession left me almost no choices. No one was hiring in Boston. The job boards were empty for someone right out of college.
Yes, I mean hindsight is 20/20 and no one could have really predicted the fallout from and during the great recession. It generally sounds like you are doing fairly well now, and that is a fantastic accomplishment. Realistically speaking there are few times in our lives where we have 'almost no choice'. The truth is that you didn't see a choice that was appealing to you, but that's just my 2c, and with inflation nowadays the 2c is worth even less hahaha.
>I am making the mean salary for my region and that mean is not what is represented in this article.
the article didn't say it was representing the mean salary though, probably the salary they are representing is the average - so there are a few high earning outliers pulling that number way up.
Or maybe, the more you are experienced and able to solve problems. Any problems. And produce quality software that performs. Then maybe the more unique that makes you. Which is not what FAANGs are looking for. They want a smart enough human, that is replaceable enough. And they will pay a lot for people that accept to play by the rules.
> My point was not that algorithms are irrelevant but they are table stakes
You're wrong. A previous team built a simple data processing pipeline for a big customer. The throughput is single digit per second. The customer was furious as they would have wait for days to get their data in. The reason is the team was not aware that their graph building and traversal algorithms are O(n^2), and they didn't understand how to distribute their processing logic. My team came in, fixed the algorithms with merely dozens of lines, and improved the throughput by more than 200 times in a matter of days. If you build large backend for data intensive systems, which pretty much every tech company needs, you will need algorithms and data structures. Probabilistic data structures? Check. Manually optimizing popular machine learning algorithms? Check. Rolling out our own implementation of graphs, trees, vector similarity search algorithms and indices, parallelization algorithms, cache oblivious data structures? Check, check, and check. You don't have to use those techniques, but your value to your projects will increase exponentially if you push the boundary.
I said algorithms are table stakes (a poker term for something you need to know just to play at the table). To continue the analogy, sounds like whoever designed that system didn't have enough to buy into the table.
> If you build large backend for data intensive systems
> This comment and those further down (particularly doubting the data) suggest that you're experiencing strong cognitive dissonance. There are, I'm sure, hundreds of engineers on HN that can testify to exactly how much FAANG pays and in which metro and how little experience they had (not including LC) before they started.
Another thing about these threads is that the hundreds of people making the big bucks are always the ones happy to confirm these numbers, and the (hundreds? thousands? who knows?) making small bucks just silently lurk and think to themselves, "I'm doing something wrong." So you get selection bias. Then one person actually has the gall to post "I'm making less, clearly doing something wrong" and he gets tons of responses like "You're underpaid, brah, go get another job!" It's just not a helpful response.
I say this as one of the lurkers who is tired of pointing out that many of us don't make these so-called average salaries, because the response is always the same: "Dude, it's data--can't argue with that. Leetcode and/or try a different company" (as if we haven't done that already).
At the risk of being trite, the response to FAANG employees being underpaid is also more or less 'you're underpaid, brah, go get another job!'. The bar for hiring and compensation is much higher, but management still wants to minimizing costs. This means systematically limiting internal salary growth, and hiring external rockstars at higher salaries to replace the internal rockstars that leave. Overall, it seems to be more effective to spend a couple months preparing/interviewing for the role you want than navigating the internal bureaucracy for an indefinite amount of time on the order of years.
>I'd love to hear a practical, actionable response to "I'm a senior FAANG who makes 75% of the "average" on levels.fyi. All other job offers I've seen are ± 2% of that. What next?"
I have literally zero idea what are you're trying to express. There's no selection bias if everyone is FAANG is very well payed and everyone outside is not.
But if you really want to see something vaguely like that then go on blind and look for posts like "I'm making 650 as an E6 where should I go from here", the idea of which I'm sure just further confounds you but I'm sorry the data is very accurate (eg inside FB/Google/AMZN there's a group that openly discusses return offers for interns and it's always within a few percent of what is on the public side).
Yea, I removed the example scenario because it's kind of pointless, in order to zero in on bias.
I don't see how there couldn't be sampling bias. Salary is a sensitive topic, and people who are making on the high range (within FAANG or outside of it) are going to be more likely to want to post about it and answer surveys about it than people in the low range. HN and Blind are obviously not random sampling. levels.fyi claims to use self-reported offer letters AND reach out directly to compensation folks at companies, which is better. But are these individual companies sharing unbiased data?
When one encounters average numbers that are a little higher than their reality, it's disappointing but believable. When one encounters average numbers that just look just crazy, one begins to ask "am I really this much of an outlier or should I suspect methodology/bias?"
> The claim that hiring is broken has been constantly repeated since ~2010 and yet all of these companies somehow, despite hiring all of the wrong non-CTO people, are still alive and are in fact thriving.
There is little correlation between the quality of your tech stack and profitability.
You could say that means hiring isn’t broken, but only from the perspective of the company.
> I'm just curious what you're expecting to have changed since the last time a post like this was made (you did say "every time I see..." so you've certainly seen many).
Hire me inflated ego without any tests, of course. God forbids I have to do anything to get a job that will put me in a bracket of earning more than 99.99% people in this world.
Just the fact "grind Leet Code" is a recommendation to someone who's most recent job title was CTO and has written two books may be a sign these interviews are broken.
But point taken, I'm sure if I studied specifically for interviews I could get those jobs. But the fact remains, a vast majority of people don't work for FAANG and even the "person unwilling to work for FAANG" example is higher salary than I have ever seen.
My point was actually that the article and ones like this seem well research but don't match the reality. For example, I recently interviewed at a fortune 100 company for a Director position and it was what this article says is "non-FAANG staff engineer" salary.