On Algorithms and Interviewing

January 17, 2019 at 10:45 AM | categories: Personal

As I write this, I'm hours away from starting to interview for full-time jobs in the software field. I've spoken with a number of recruiters and hiring managers and have received interview preparation materials from a handful of companies, many of which you've probably heard of.

I was hoping things would have changed since I last seriously underwent this endeavor ~7.5 years ago (I did interview periodically when I was at Mozilla in order to test waters, keep my interview skills sharp, etc). But it appears the industry is still generally fixated on algorithms and data structures in interviews. The way algorithms and similar coding tricks are emphasized in the preparation materials I've received, you'd think people in software spend a major part of their work days thinking about and implementing algorithms. But from my experience, this is very far from the case! So why are so many companies and interviewers fixated on algorithms. And is this a good thing?

When they matter, efficient algorithms, data structures, and other tricks are important and useful skills to have. But from my experience, they matter far less than you would think. If I were to make a list of important job skills and traits for software and programming, memorized knowledge of algorithms and data structures is so far down the list that I don't think I would even ask about algorithms fundamentals for most job candidates! (In fact I don't.) I think it is vastly more important to focus on behavioral qualities and potential to actually think and apply knowledge rather than regurgitate it. Algorithms and data structures, after all, are learned knowledge. All other things be equal, I'd rather have someone who knows when to ask for help with an algorithms issue or can pick up the skill than a curmudgeon algorithms genius who has an abrasive personality and clings to old habits.

In the spirit of full disclosure, I should to state that my algorithms skills are relatively weak. You can accuse me of writing this post to fulfill my own selfish interests. You wouldn't be wrong. But I know there are others like me who are good at programming yet struggle with algorithms and question the utility of algorithms in interviews. I'm attempting to write this post for all of us.

I have failed job interviews because the interviewer assessed my algorithms abilities as weak. I'm able to work through this deficiency with interviewers who care more about the behavioral traits I exhibit when in such a situation (I try to be quick about admitting my technical weaknesses and to ask for help when needed). But some interviewers aren't as interested in the behavioral traits or insist on a baseline level of memorized algorithms knowledge beyond my own. I feel like my relative algorithms weakness hasn't hurt me on the job, as I hardly find myself caring about algorithms in the work I do. In the majority of cases, the choice of an algorithm just doesn't matter for the size of the data set. Or a standard algorithm or data structure available in the standard library of the language I'm using is good enough. In the cases where I realize algorithms and data structures would matter, I run my technical questions past someone with more knowledge in the domain than me. Or if I don't do that, it often comes up during code review. Without strong algorithms and data structures knowledge, I'm able to maintain the Firefox build system, become a core contributor to a version control tool (something you think would require a lot of heavy algorithms knowledge), maintain various open source projects, diagnose and address low-level performance issues in complex software and systems. About the only impact that being weak in algorithms and data structures has had on my career is that some companies passed on hiring me because they perceived strength in this area to be important.

Albert Einstein once said, I never commit to memory anything that can easily be looked up in a book. A modern adaptation of that quote may go something like, never memorize how to implement an algorithm or data structure when you can just Google it or use a software library implementing it. If you have knowledge of how to implement various algorithms in your head, that's good for you, I suppose. But I think the bigger brain knowledge to possess is when algorithms matter and to a lesser extent, what types of algorithms are appropriate for particular problems. Answering these problems requires critical thinking. Actually implementing algorithms, by contrast, merely requires knowledge that can easily be looked up in a book (the algorithm or data structure itself) coupled with some programming knowledge for how to apply it. A capable programmer will be able to do both these things and pick up algorithms and data structure knowledge on the job, if necessary.

Some would say that algorithms are a good way to flush out coding ability. And coding ability is important to assess as part of interviewing a job candidate for a programming position! They aren't wrong. But there are much better ways to receive stronger signals about an interviewee's compatibility! On the coding front, there are infinite ways to assess programming capability without involving algorithms. So why involve algorithms as part of the interview?

One way I approach interviewing people is to imagine what the typical work day of that role will be like. How much time do they spend coding, investigating bugs, debugging, attending meetings, writing proposals, politicking with managers, etc. This produces a conceptual pie chart of that role's activities. I then try to structure the interview such that the topics covered in the interview correlate with and somewhat in proportion to activities in that job role. Is the role a heads down junior coder? A team lead or manager? When you start trying to map the time in various areas of the role to time spent in the interview, you realize that the common technical interview overly emphasizes some areas and often completely ignores others! One of the areas that is over-emphasizes is algorithms. Again, your typical programmer is going to be spending most of their typical day doing things unrelated to algorithms. So why are you spending precious interview time asking about algorithms when you could be probing an area that actually correlates to typical job activities? When viewed through this lens, the prevalence of algorithms in interviews just doesn't make much sense to me.

Perhaps knowledge of algorithms should be basic knowledge that every programmer should possess. If so, then asking about algorithms is fair game during an interview, I suppose. But I'm not comfortable with this line of thought.

I've always found it fascinating the ways that people with different backgrounds and degrees approach problems differently. From my experience, some of the best ideas and perspectives come from people with backgrounds and degrees which are minorities in the field. I've worked with programmers with degrees in philosophy and history who were some of the best programmers and overall minds in the room. One of the great things about software and programming is it is accessible to anyone, regardless of background. If you can code, you can land a (usually high-paying) job. Yes, the field is highly technical. But you don't need formal education or a degree to enter it like you do similar high-end professions, such as medicine or law. You can argue whether this is a good thing or not. But I think the accessibility of the software profession - the lack of formal gatekeeping - is something to marvel at, something that we as an industry should embrace and be proud of. Do arbitrary hurdles to joining the industry help or hinder it?

A problem with emphasizing algorithms in interviews is that algorithms are somewhat highly specialized and academic. There are entire areas of programming and software where detailed knowledge of algorithms just isn't that important. The bar for so much software is it works and it quite frankly doesn't matter if you have a quadratic algorithm instead of something better.

Most people I know are exposed to algorithms fundamentals during their university education as part of pursuing a degree in computer science or engineering. You almost certainly aren't going to have academic exposure to algorithms if you are say a liberal arts major - never mind someone who doesn't attend university at all (I also know plenty of terrific programmers who don't have degrees). From my own experience, my degree is in computer engineering. Not computer science or software engineering: computer engineering. I remember from my university days that my computer science friends seemed to have a much better grasp at algorithms and theory of software and programming than I did. When I was taking classes about how hardware and electronics work, they were learning all about the mathematical concepts underpinning the field, different approaches to programming language design, etc. I received very little of that. And on top of that, I struggled with my single algorithms course at university. So I entered the workforce without as good of a grasp on the computer science fundamentals as others I knew. (But I still probably knew more than someone in an unrelated field.) The point I'm trying to make is that because algorithms are somewhat highly specialized and academic in nature, requiring knowledge in algorithms will effectively bias your hiring towards people with strong computer science backgrounds. Stated another way, screening on algorithms knowledge undermines diversity and inclusion initiatives by excluding viable candidates who don't have strong backgrounds in computer science. Sure, if someone wants to enter the industry they can take the time to study up on algorithms. But why force them to do that? It feels like arbitrary gatekeeping given the relative non-importance of algorithms given the typical activities of the typical programmer. So why do it?

I suspect major contributing reasons to why algorithms are so prevalent in interviews are cargo culting, laziness, and lack of formal interview training / caring about diversity. As an industry, the software field is pretty bad at applying best practices and learning from our mistakes. I suspect this will change once the relatively young industry catches up to more-established industries and we're forced to cope with the realities of legal and monetary liabilities the way practically every other industry is. (We're starting to see this with monetary damages for security breaches.) Anyway, we as an industry are pretty bad at self-regulating and adopting practices with proven benefits. We like to settle for what is known. Laziness and the comfort associated with is easy. Seeking out and implementing change is harder. This is human nature. We see this with well-known people in industry rejecting the ideas of continuous testing (years ago) or fuzzing (more recently). We see it in C/C++ programmers who are delusional about their abilities to write secure code and decry e.g. Rust's safety guarantees as superfluous. The industry is disproportionately white and male (at least in the United States). And this brings with it certain personality tendencies. One is a macho attitude, which can manifest in interviews via the interviewer embarking on an ego trip proving they know some esoteric algorithm or data structure the candidate does not.

As a clear example of this, Google was known for asking brainteaser interview questions. (The practice may have been prevalent at Microsoft before Google was the darling of Silicon Valley, but that was before I entered industry.) This trend caught on and soon companies all over were asking brainteasers! The problem was that these questions didn't correlate to actual job performance! From a 2013 NYTimes interview with Google's VP of People Operations:

On the hiring side, we found that brainteasers are a complete waste of
time. How many golf balls can you fit into an airplane? How many gas
stations in Manhattan? A complete waste of time. They don’t predict
anything. They serve primarily to make the interviewer feel smart.

But the damage was done. I still heard these kinds of questions when interviewing in the wild long after Google realized they were bad questions and instructed interviewers not to ask them. I even believe I got a brainteaser when interviewing at Google after the supposed banning of these types of questions! And I won't be shocked if I'm asked a brainteaser in 2019 as part of the several interviews I'll be doing in the days ahead.

Asking questions with no correlation to job performance because a popular company asked that type of question for a while: that's textbook cargo culting. Failing to change your ways despite evidence saying you should: laziness. Insisting that your way is correct and others need to be like you: gatekeeping.

I'm not saying algorithms and data structures during interviews are intrinsically bad and that we should stop asking about them. What I am saying is that we as an industry need to examine how we interview. We need to invest in scientifically proven techniques. (Research shows that behavioral interview questions are better. Tell me about a time when, etc.) And after more than ten years in industry, my experience tells me that interviews place a disproportionate emphasis on algorithms and data structures compared to the daily activities of the typical programmer. And on top of that, due to their academic nature, I worry that screening for algorithms and data structures knowledge is undermining the diversity and inclusivity of our field by biasing towards people with strong computer science backgrounds. I think it is time we examine the role of algorithms and data structures in interviews and consider focusing on other areas instead.

Seeking Employment

January 07, 2019 at 03:25 PM | categories: Personal, Mozilla

After almost seven and a half years as an employee of Mozilla Corporation, I'm moving on. I have already worked my final day as an employee.

This post is the first time that I've publicly acknowledged my departure. To any Mozillians reading this, I regret that I did not send out a farewell email before I left. But the circumstances of my departure weren't conducive to doing so. I've been drafting a proper farewell blog post. But it has been very challenging to compose. Furthermore, each passing day brings with it new insights into my time at Mozilla and a new wrinkle to integrate into the reflective story I want to tell in that post. I vow to eventually publish a proper goodbye that serves as the bookend to my employment at Mozilla. Until then, just let me say that I'm already missing working with many of you. I've connected with several people since I left and still owe responses or messages to many more. If you want to get in touch, my contact info is in my résumé.

I left Mozilla without new employment lined up. That leads me to the subject line of this post: I'm seeking employment. The remainder of this post is thus tailored to potential employers.

My résumé has been updated. But that two page summary only scratches the surface of my experience and set of skills. The Body of Work page of my website is a more detailed record of the work I've done. But even it is not complete!

Perusing through my posts on this blog will reveal even more about the work I've done and how I go about it. My résumé links to a few posts that I think are great examples of the level of understanding and detail that I'm capable of harnessing.

As far as the kind of work I want to do or the type of company I want to work for, I'm trying to keep an open mind. But I do have some biases.

I prefer established companies to early start-ups for various reasons. Dan Luu's Big companies v. startups is aligned pretty well with my thinking.

One of the reasons I worked for Mozilla was because of my personal alignment with the Mozilla Manifesto. So I gravitate towards employers that share those principles and am somewhat turned off by those that counteract them. But I recognize that the world is complex and that competing perspectives aren't intrinsically evil. In other words, I try to maintain an open mind.

I'm attracted to employers that align their business with improving the well-being of the planet, especially the people on it. The link between the business and well-being can be tenuous: a B2B business for example is presumably selling something that helps people, and that helping is what matters to me. The tighter the link between the business and improving the world will increase my attraction to a employer.

I started my university education as a biomedical engineer because I liked the idea of being at the intersection of technology and medicine. And part of me really wants to return to this space because there are few things more noble than helping a fellow human being in need.

As for the kind of role or technical work I want to do, I could go in any number of directions. I still enjoy doing individual contributor type work and believe I could be an asset to an employer doing that work. But I also crave working on a team, performing technical mentorship, and being a leader of technical components. I enjoy participating in high-level planning as well as implementing the low-level aspects. I recognize that while my individual output can be substantial (I can provide data showing that I was one of the most prolific technical contributors at Mozilla during my time there) I can be more valuable to an employer when I bestow skills and knowledge unto others through teaching, mentorship, setting an example, etc.

I have what I would consider expertise in a few domains that may be attractive to employers.

I was a technical maintainer of Firefox's build system and initiated a transition away from an architecture that had been in place since the Netscape days. I definitely geek out way too much on build systems.

I am a contributor to the Mercurial version control tool. I know way too much about the internals of Mercurial, Git, and other version control tools. I am intimately aware of scaling problems with these tools. Some of the scaling work I did for Mercurial saved Mozilla tens of thousands of dollars in direct operational costs and probably hundreds of thousands of dollars in saved people time due to fewer service disruptions and faster operations.

I have exposure to both client and server side work and the problems encountered within each domain. I've dabbled in lots of technologies, operating systems, and tools. I'm not afraid to learn something new. Although as my experience increases, so does my skepticism of shiny new things (I've been burned by technical fads too many times).

I have a keen fascination with optimization and scaling, whether it be on a technical level or in terms of workflows and human behavior. I like to ask and then what so I'm thinking a few steps out and am prepared for the next problem or consequence of an immediate action.

I seem to have a knack for caring about user experience and interfaces. (Although my own visual design skills aren't the greatest - see my website design for proof.) I'm pretty passionate that tools that people use should be simple and usable. Cognitive dissonance, latency, and distractions are real and as an industry we don't do a great job minimizing these disruptions so focus and productivity can be maximized. I'm not saying I would be a good product manager or UI designer. But it's something I've thought about because not many engineers seem to exhibit the passion for good user experience that I do and that intersection of skills could be valuable.

My favorite time at Mozilla was when I was working on a unified engineering productivity team. The team controlled most of the tools and infrastructure that Firefox developers interacted with in order to do their jobs. I absolutely loved taking a whole-world view of that problem space and identifying the high-level problems - and low-hanging fruit - to improve the overall Firefox development experience. I derived a lot of satisfaction from identifying pain points, equating them to a dollar cost by extrapolating people time wasted due to them, justifying working on them, and finally celebrating - along with the overall engineering team - when improvements were made. I think I would be a tremendous asset to a company working in this space. And if my experience at Mozilla is any indicator, I would more than offset my total employment cost by doing this kind of work.

I've been entertaining the idea of contracting for a while before I resume full-time employment with a single employer. However, I've never contracted before and need to do some homework before I commit to that. (Please leave a comment or email me if you have recommendations on reading material.)

My dream contract gig would likely be to finish the Mercurial wire protocol and storage work I started last year. I would need to type up a formal proposal, but the gist of it is the work I started has the potential to leapfrog Git in terms of both client-side and server-side performance and scalability. Mercurial would be able to open Git repositories on the local filesystem as well as consume them via the Git wire protocol. Transparent Git interoperability would enable Mercurial to be used as a drop-in replacement for Git, which would benefit users who don't have control over the server (such as projects that live on GitHub). Mercurial's new wire protocol is designed with global scalability and distribution in mind. The goal is to enable server operators to deploy scalable VCS servers in a turn-key manner by relying on scalable key-value stores and content distribution networks as much as possible (Mercurial and Git today require servers to perform way too much work and aren't designed with modern distributed systems best practices, which is why scaling them is hard). The new protocol is being designed such that a Mercurial server could expose Git data. It would then be possible to teach a Git client to speak the Mercurial wire protocol, which would result in Mercurial being a more scalable Git server than Git is today. If my vision is achieved, this would make server-side VCS scaling problems go away and would eliminate the religious debate between Git and Mercurial (the answer would be deploy a Mercurial server, allow data to be exposed to Git, and let consumers choose). I conservatively estimate that the benefits to industry would be in the millions of dollars. How I would structure a contract to deliver aspects of this, I'm not sure. But if you are willing to invest six figures towards this bet, let's talk. A good foundation of this work is already implemented in Mercurial and the Mercurial core development team is already on-board with many aspects of the vision, so I'm not spewing vapor.

Another potential contract opportunity would be funding PyOxidizer. I started the project a few months back as a side-project as an excuse to learn Rust while solving a fun problem that I thought needed solving. I was hoping for the project to be useful for Mercurial and Mozilla one day. But if social media activity is any indication, there seems to be somewhat widespread interest in this project. I have no doubt that once complete, companies will be using PyOxidizer to ship products that generate revenue and that PyOxidizer will save them engineering resources. I'd very much like to recapture some of that value into my pockets, if possible. Again, I'm somewhat naive about how to write contracts since I've never contracted, but I imagine deliver a tool that allows me to ship product X as a standalone binary to platforms Y and Z is definitely something that could be structured as a contract.

As for the timeline, I was at Mozilla for what feels like an eternity in Silicon Valley. And Mozilla's way of working is substantially different from many companies. I need some time to decompress and unlearn some Mozilla habits. My future employer will inherit a happier and more productive employee by allowing me to take some much-needed time off.

I'm looking to resume full-time work no sooner than March 1. I'd like to figure out what the next step in my career is by the end of January. Then I can sign some papers, pack up my skiis, and become a ski bum for the month of February: if I don't use this unemployment opportunity to have at least 20 days on the slopes this season and visit some new mountains, I will be very disappointed in myself!

If you want to get in touch, my contact info is in my résumé. I tend not to answer incoming calls from unknown numbers, so email is preferred. But if you leave a voicemail, I'll try to get back to you.

I look forward to working for a great engineering organization in the near future!