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one word. or 4 letters. understand them and you understand everything else about this issue:


ie. they have such an extreme scale that often what's best for them is also unique to them, so doesn't really work/apply/be-appreciated anywhere else. They want sharp folks. But because so many, and because folks at top are so sharp, and because their scale is so gigantic, they want sharp drones. Color inside the lines, just really really really fast and perfect. Getting something 1% wrong costs them millions more than otherwise. Which is different than the mindset of the vast majority of other companies where getting something even 91% right means making millions of dollars more for the shareholders, than otherwise. The rest is details.

It might apply to some positions like SRE, but surely for an engineer working on one of their mobiles apps, there are still many problems to solve.

Google seems perfect for two kinds of programmers/engineers:

1. you are 20-something, white or Asian, fresh out of college, Jewish/Stanford/Ivy/otherwise-2nd-gen-pampered-background, male, maybe still in college or 1st startup AND you are willing to move anywhere they want, do anything they tell you, you have no spouse, no kids, no ill parents, no local geo investments, no major illnesses, not multi-careered, you're still impressed by Shiny/Words, etc.

2. you are 30/40-ish but now established as a Major Name (Linus, Vint, Guido, etc.) and/or owner of a company Google wants to buy, and therefore $M+ talks loudly

if you don't fit (approximately) into one of those two boxes, then Google is a non-ideal fit for you

META: downvote me all you want HN, I do not care what the GroupMind's Allowed Opinion algorithm here thinks anymore

Since this guy has no clue, let me clarify as someone who worked at Google until recently:

Any Google employee who spews something like "Google is perfect if you are 20-something, white or Asian, Jewish/blah blah background male..." will be (with high probability) asked by his[1] coworkers WTF he is talking about, and would he please shut up or learn to open his mouth without insulting his coworkers. If the behavior continues, it may or may not result in disciplinary action, though the consensus seemed that there aren't enough disciplinary action. (I guess it depends on who you ask.)

Contrary to popular conception, Google employs a lot of thoughtful people and they want to make their workplace a better place to live and work.

[1] I'd normally say "their", but it seems "his" here is more coherent with my... "observation of reality."

"Any Google employee who spews something like" -- I don't see that he was commenting on what Google employees say.

May I ask which Google location you were/are located at?

Mountain View. (DISCLAIMER: It's a vast office space, so different part of the organization might have different cultures. My perception is from my personal experience as well as from reading through internal Google+, mailing lists, etc.)

Hearing this from a korean that moved to US is funny :-)

How about category 3: 30+, has a family, wants a stable job with good pay, but not crazy hours?

Ding ding ding! That's me to a T. I'm in my early 30s, with a family, and just started in MTV this past Monday. I spent 11 years at a small company in Cleveland, and had reached the top of the engineering ladder there. Had the opportunity to move out to Silicon Valley, where I have much more varied career prospects if I decide Google isn't for me after my shares vest.

On my team are two very experienced engineers (one's been at Google for longer than all but 38 employees), and it will be a great opportunity to learn from some really great people and grow my skills, which had been stagnating due to lack of challenge at my last company.

There was never any pressure at my last job to work crazy hours, so that wasn't really a factor in moving per se; but Google is big on work-life balance, which is super important to me.

good point. reasonable. the making of generalizations does not outlaw other generalized groups or categories.

an entire side discussion about Bayes predictions could be entered here. where having a few rough rules would yield a 90% beneficial prediction. add a 2nd rule or exception boosts your yield to 95%. add a 3rd rule/exception boosts your yield to 97%. and so on...

Why does your race matter in the first point?

Probably just observation.

I've been to google campus on Mt. View and LAX Google (which is really in Venice...).

They're mostly Asian and White. On a Saturday I was visiting a buddy and I swear it was 90% Asian.

Race doesn't really matter, it just that it happen to be there are just many Asian and White in the tech fields.

observation of reality. not theory. not wish/want/should/ought. actual reality.

trust me, if you're smart and wise then the older you get you should be getting more and more reality-based in your decisions. not theory. not ideal. not PC. reality.

Your race has nothing to do with whether or not it's a good place to work as far as happiness goes. I am about as anti-PC as it gets. I can see you are a jaded individual but that's no excuse to post garbage that has no credible evidence backing it.

Do you have any data to support that?

Google has their own page on employee diversity:


If you click the "Tech" tab, they self-report as 59% White, 35% Asian, 2% Hispanic, 1% Black. Note that the ethnicity data is US-only (the gender data is global).

That a lot of white and Asian people work there isn't evidence that it is only a good place for white and Asian people to work there, however.

Indeed, you'd need to ask non-white, non-Asian employees about their experiences there.

Here's a post from November 2014 about a black woman's experience at Google: https://medium.com/thelist/the-other-side-of-diversity-1bb3d...

It's a very interesting read, I recommend it.

>Indeed, you'd need to ask non-white, non-Asian employees about their experiences there.

Well, seeing as I am a non-white non-Asian former Google employee....

Using "you" in the indefinite sense. :) https://en.wikipedia.org/wiki/Generic_you

Interesting link (even if it is a rather depressing confirmation of typical white tech-worker douchebagginess).

The wording of:

> When I transferred to my second team there, Desktop Support, diversity lightning struck: I was a black woman reporting to another black woman in a technical role. Moreover, our team was predominantly black.

strikes me as a bit funny. Clearly "diversity lightning" implies an interesting chance outcome, facilitated by the possibility of there being similar people around, but "a black woman reporting to a black woman, in a predominantly black team" is stretching "diversity" to mean non-white (or more probably, non-white, non-male, non-gay).

I completely understand (in an entirely outside-looking-in way) the authors perspective -- but in my book a "diversity success story" would've been if the co-workers at Google hadn't been close-minded fucks, pardon the expression.

Now, I still think positive (hiring) discrimination is one of the best ways to achieve a mixed/diverse team, and that in turn is one cornerstone for a diverse and tolerant culture.

But sometimes you find yourself in the cultural stone age, and it's hard to see a good way out. Sounds like Google California was one such place -- not just due to Google, but apparently due to something (real equal opportunity) missing from higher education in the US in general?

Others might also find:


relevant, interesting. And a bit more positive to boot.

Basically it comes down to her not feeling comfortable being around a majority of whites and asians and she feels that she has to change her whole personality just s others like her more.

She also prefers to be around blacks, preferably black women.

This has little to do with Google though.

Google is rather corporate and thus it's only for people willing to adapt a corporate personality during the day - often in combination with a rather conservative physical appearance.

Cornrows and overly casual probably wouldn't fly too well in the head office, no matter if you're black, white or asian.

She should just get another job.

Also note the survey of non-Google experiences in that article. Google vs the rest of the for-profit tech biz is one question, business vs non-profit?/charity is another.

Non profits and charities can discriminate far easier than a for profit business.

Agree. I've spent a lot of time in tech, biotech and hanging around the medical community and it's no secret that certain racial & ethnic groups are overrepresented here in the US vs the standard population.

Why that is and if that's something to be concerned about are separate questions which I think are out of scope here, but it certainly doesn't necessarily imply that you need to conform to have an enjoyable work experience.

Thanks for the link. I don't however see any evidence to support the initial assumption that it's "perfect" for those races.


I did not. A single persons observations are not indicative of even a general 'reality', nor a convincing data set.

While I didn't love your tone throughout, I downvoted for the "META" comment.

I'm glad that you spoke your mind however unpopular even though I disagree with some of your findings/conclusions but it's really brave of you to stand up to the downvote goons on HN that all they care about is social engineering, suppressing free speech and stifling constructive debate.

I awake. It is dark.

Therefore when I awake it is always dark.

Problem. Mismatch.

Turns out that if I awake between the local hours of 5am and 7pm then it is light. Otherwise it is dark. Problem. Mismatch. Turns out, it depends on the "time zone". Also turns out, depends on whether I'm sleeping inside or outside. In a hotel room or tent. Whether in a tent or in a building room with blinds. Etc. Etc. Each devil-in-the-details helps refine the case even further. But the "bet" to make is always the most "correct" bet to make, based only on the evidence observed to date, at hand. Thus Bayes.

Thus the Turing award.

It's just as perfect and reliable as that. And just as imperfect or vulnerable as that.

Bayes is perfect but also painfully bloodily sharp-edged. Best description I have.

I'm in the middle of designing and building a system which uses Bayesian models.

One thing that struck me early is that while Bayes itself is rock solid, like arithmetic, when you go to apply it the results live or die on the quality of the models, and the relevance/realism of the evidence used to train them. GIGO.

But once you do have a good, relevant, signal-producing model, then, using it is a bit like doing a multi-dimensional lookup, or function call. Conceptually easy to understand, and, in many cases (depending, of course, on the details) cache-friendly.

"all models are wrong, but some are useful" - Box.

I think the Bayesian approach is a good place to start, and provides a coherent way to think about things.

Pragmatically, one might end up needing to introduce a few approximations into the model, to make it computationally tractable, for example, but it is good to be able to view this in the context of what the gold-plated theoretical modelling approach would be.

Instead of doing something ad-hoc that appears to work, say.

You can also augment the state to take this into account.

I have a model that says my system does F with Q amount of uncertainty, and my measurements are Z with R uncertainty. But I have to give precise numbers for R, when it is just an imprecise model or SWAG. I can add to my state a parameter for how precise R is, and let the filter estimate it over time. Not always, and it is noisy, but it can be done.

There are other approaches - use a filter bank, each with a different set of assumptions. Run 'em all, and either pick one or blend them, depending on your scenario. 'Depending' being the topic of many a PhD thesis, but again, very doable in practice for many problems.

> There really is no excuse. I should get off my ass and make something.

please don't make a Bitcoin exchange/bank. or anything that will take enduser's private PII or money.

the mindset to "just go make something" is admirable in some cases. on others it's not. scratching your own itch and then monetizing it is admirable. picking up a knife and thinking therefore that one can be a neurosurgeon now, is not.

too many people do the latter. the former is fine. ;-)

Couldn't agree more. I've thought about this a lot, how the current mindset is creating a bunch of people who see the end state as "a unicorn" and nothing else is good enough. Anything else is "failure".

Me, I'd be happy if I could find a little niche to fill, and if it could pay me a very comfortable wage that allowed me to retire in 5 or so years. I'm 38 now - half a million a year for a couple of years, then sell whatever it is for another couple of million, that'll do me very nicely thank you very much.

Does that sound like a lifestyle business? Damn right. That's exactly what I want. (I'm in Australia, BTW - the whole Silicon Valley, work 23 hours a day, all that crap - no thanks. I value my life and my friends at way more than a billion dollars.)

Half a million a year in profits / wages is definitely a bit beyond lifestyle business. In wages it's possible top of field in something that really matters to a large enterprise. In profit, it's a whole lot of work in a very specific and high value area.

Yep, I saw it take about 15 years trying and succeeding to build a web hosting business before the owner / founder was getting near that for themself. Software can be faster, but that's still extremely lucky and/or lots of hard work.

>Does that sound like a lifestyle business?

No, it doesn't. It sounds like a 3 SD event.

A couple million in a couple of years sounds like a unicorn, actually.

two words: survivorship bias

I agree that complexity is far up there. But also risk. Also long term thinking. And net cost or net profit. The more years I have under my belt, I think more and more not only about complexity, but also risk, cost, profit. Code and hardware is just a means to an end. Not the end itself.

But yes, seek the minimum amount of complexity to materialize the inherent, necessary complexity. But don't allow a drop of complexity more than that. Architecture astronauts, pattern fashionistas, I'm looking at you. KISS. Spend your complexity dollars where it gives you something you truly need or want. Don't do things Just Because.

seeing this comment on HN, made my PG, a Lisp book author, is amusing. I have to admit that while I don't do Lisp day-to-day probably my favorite Lisp book(s) were written by him. The practical hacker in me prefers Python, Java and C.

But the elegant hacker in me? Prefers Lisp. And PG captured that in his writing.

IDK where the elegant but impractical (as it was a trade off). CL is a practical language. Consider loop, format, multiple values or the standard methd combination in CL.

While the only practical thing about python is that it has more libraries. Nevermind the fact that python scope is misdesigned even in Python 3!

reporting in. and I remember the time before Byte existed.

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