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"Such as "On a scale of 1-10, 1 being novice, 10 being creator of said technology, how would you rate yourself?" Then based on this answer I'll ask a question at that level. I find that most people screened don't actually know the basic fundamentals of the technologies they list."

See, and I hate questions like that.

If you're going to ask candidates to rate themselves in a technology, don't ask for a number. What I think a 7 is might be different from what you think it is. Just ask the candidate how comfortable they feel in a language. Words work better than numbers.

Also, you should know that many candidates are really scared by someone asking them to rate themselves on a scale from 1 to 10. You're kicking off an interview with a degree of intimidation that's probably unnecessary.

You're right though that many people don't know the fundamentals of the technologies they list. This doesn't mean that they're bad engineers; there's just a lot of confusion around at what point you should list a technology on your resume.

Remember that a lot of companies aren't particularly looking for competency in a particular language. Thus, drilling into the specifics of a technology doesn't assess much for them.

I'm always answering “7” or “4”.

I've found that algorithms are one of those things that doesn't come up often in your daily work, but when you need them, you really need them, and otherwise you'll end up delivering a substandard product. It's very much like Joel Spolsky article on hitting the high notes:

http://www.joelonsoftware.com/articles/HighNotes.html

I've found that in my work, a tricky algorithmic problem comes up on average about once a month. It's a tiny fraction of the total work I do - but if I couldn't solve algorithmic problems, then there are whole projects that I simply couldn't tackle. We'd either do something substandard for users, or we'd throw bodies at the problem (which still happens a lot, unfortunately), or they'd need to bring in someone else to do my job.

Moreover, I've found that there's a cultural shift that happens when enough people are familiar with algorithm design within a company. A lot of other places I've worked are basically "Make the user fill out a form, dump it in a database, format it for display." When a significant fraction of your employees are capable of hitting the high notes, this becomes "Make the user interact with your system in the manner they're most comfortable with, extract meaning out of their ordinary behavior, compute interesting results, and show it to them." The complexity moves from the user to the software, and as a result, users would rather use your software. There are whole subsystems within Google Search - synonyms, spelling, refinements, authorship, snippets, ranking, translate, voice search, etc. - that would never have happened if there weren't a critical mass of people willing to dive into difficult problems.

How do you account for the Dunning-Kruger[1] effect when asking your "How do you rate yourself?" question? I would guess that answers in the range "6 or 7" could span a really wide range of actual knowledge. Does that agree with your experience?

[1]http://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect

I understand that he just asks real question on that level to calibrate your answer. It's just faster to start from some reference point.

Right, even if someone miscalibrated we can quickly get to the proper calibration. Though, I stress ten as being the creator of said technology. For example, in the case of PHP ten would entail actually being able extend PHP via C/C++. In my experience most people calibrate pretty well initially or after an initial miscalibration.

Ask for code. TALK about algorithms at a high level. I have an aptitude for reading people, but my day two impressions of coding with a new person tend to lead to valid assumptions about them months later.

I don't see how I'm perpetuating the problem. I'm just explaining what Google actually does and what they don't do.