Google's “Director of Engineering” Hiring Test

I'm amazed he knew things in such detail. I mean who would know just how long a MAC address is? Or what the actual SYN/ACK etc tcp flags are? You just need to know what they're used for, and if you need the specifics, you'll find out with a single search. He seemed to know that as well though. Kernighan for bit twiddling algos, that kind of thing.

It's a bit strange to have someone non-technical interviewing a techie. You end up with stupid discussions like the one about Quicksort. If you point out qs is one of several things with the same big-O, you'll probably also get it "wrong". But the real problem is that a guy who is just reading off a sheet can't give any form of nuanced feedback. Was the guy blagging the sort algo question? Did he know if in detail? Does he know what the current state of research on that area is? There's no way to know that if your guy is just a recruiter, but I'm sure even a relatively junior coder would be able to tell if someone was just doing technical word salad.

I wonder what would happen if ordinary people recruited for medical doctor jobs? Would you be comfortable rejecting a guy who'd been in medical school for 10 years based on his not knowing what the "funny bone" is? Wouldn't you tell your boss that you felt a bit out of that league? It's amazing you can get someone to do this without them going red in the face.

I knew all these answers too, because I was a developer in the 1990s.

There is absolutely no purpose to knowing off the top of your head how long an ethernet address is, or even what system call will retrieve an inode (his bickering over stat() "filling in" rather than "returning" was bogus, for what it's worth). The top Google search result for each of these questions has the answer. Knowing these things isn't part of being a practicing programmer; knowing how to find out is.

But what if Google is down and you are tasked with diagnosing it?

I lost it at this one. As if there is some single point of failure that's going to bring all of Google down, and some intrepid director of engineering has to inspect some TCP packets by hand to fix it.

This is where I threw up my hands too. The Director of Engineering does not need to know the difference between SIGTERM and SIGKILL, or how many bytes are in a MAC address. I guess it's a nice bonus if he does, but he'll spending 10 hours per day in meetings talking about roadmaps, shielding his team from the execs, and removing productivity roadblocks. "Third engineer from the left" is doing the packet inspection--ask HIM about SYN and ACK.

Reading more closely, it sounds like they are not interviewing him for a director of engineering position; it just sounds like he thinks his current role, CEO-who-writes-code of a very small software company (http://www.gwan.com/about), qualifies him for a director-of-engineering-level position. He's probably being interviewed for an SRE team lead or thereabouts.

Why he's being interviewed for that position is a different question entirely, and I can imagine Google being totally right or totally wrong.

How can you imagine Google being totally right here? The disconnect between the questions being asked and the interviewer's lack of knowledge made the interview a waste of time no matter WHAT role they are interviewing for.

Take, for example, the sorting question. "Why is QuickSort the best sorting algorithm?" The answer being looked for was, "It has the best Big O."

And this is wrong. Its average case is O(n log(n)). Its worst case is O(n^2). Which do you call its big-O? Moving on, the average case of O(n log(n)) is matched by a wide variety of sorting algorithms. How do you choose one?

Here is a better answer.

QuickSort is a very simple to implement algorithm which achieves the lowest average number of operations on a randomly sorted list. Which is why it is so widely adopted despite sometimes being very slow.

However Timsort appears to be the fastest general purpose sorting algorithm for the mix of random and partially sorted lists seen in practice.

When I tend to notice that sorting is slow, generally that's a larger workload where some type of merge sort would be appropriate.

> Its average case is O(n log(n)). Its worst case is O(n^2). Which do you call its big-O?

I know this is irrelevant to the larger point of your post, and I'm sure that you know this already, but the worst case is the big-O. This is just another reason why "big-O" is not the most helpful thing to discuss in practice.