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.
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.
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).
- 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.
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.
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.
Hey just a theory. I am probably wrong.
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
I have. Many.
Depends on how extensive your processing is of course.
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).
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).
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?"
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.
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.
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.