Cultural Ramifications of Technical Interviews

Technical interviews are widely used from the smallest of start ups to the greatest of tech giants. They are thought of as an objective process, providing a fair means of evaluation to all. Yet appearances can be deceiving: in addition to harming diversity in the field, technical interviews promote a culture of disciplinary obedience for technical employees. By judging and defining the worth of a prospective employee by a set of technical questions that is typically far removed from their field of speciality, experience, and job requirements, we foster a culture that seeks to increasingly discipline both new and experienced technical employees.

Veiling Disciplinary Obedience

Technical interviews are often designed to examine the knowledge and manipulation of data structures and algorithms. One is expected to be familiar with numerous data structures, their time and space complexity, and their usages. Interviews can often range from requiring an intricate implementation of a data structure with increasingly strict constraints, to being able to identify with ease which data structure is most suitable for a tricky problem. A typical Computer Science undergraduate is likely to have taken one or two courses relevant to this particular subject; however, the skills obtained and practiced beyond the classroom walls differ significantly. Some specializations that often require a skillset vastly disjoint from data structures and algorithms include DevOps, SysOps, Systems Architect, Systems Administrator, Security Engineer, and all positions pertaining to Research and Development. Yet, those employed in these specializations must still endure interviews that heavily test their knowledge of data structures and algorithms, their expertise dismissed in vain despite its crucialness in the tech sector.

Photo CC-BY Michael Himbeault, filtered.

Take, for example, a professor who takes on a role in industry after an accumulation of prestige in their research area. More often than not, such a role aligns with their research interests, yet they are subjected to the same interview process as a newly graduated college student. It is demeaning and senseless to dismiss research accomplishments and merit simply because an interviewee failed to navigate a standard set of technical questions disjoint from their specialization. Such behavior not only urges technical candidates to persistently prove themselves, but requires them to commit an extraneous amount of effort into it, with those who have not been “interviewing often” urged to practice for “3 months with 20 hours prep-time per week”, essentially equivalent to a part-time job.

Gayle Laakmann McDowell, Founder and CEO of CareerCup and the author of Cracking the Coding Interview, echoes the mantra that technical interviews are objective, level the playing field, and give a leg-up to those who started later. She additionally defends that technical interviews are beneficial, and not harmful, to women. What is often not acknowledged is that this belief only serves to veil a culture of disciplinary obedience in the name of objectivity. Using punishing and irrelevant interview processes can often induce feelings of shame, insecurity, and discomfort, as forcing interviewees to take extraneous means of preparation can lead to great cognitive dissonance and distress. In order to reduce the dissonance between the belief of having a highly valuable and specialized skillset and the behavior of having to prove one’s merit in a way disjoint from one’s specialization, we often lessen the emphasis on the former, and thus end up feeling insufficient and indebted to our prospective employers. In order to succeed in the interview process, we must acquire additional training and skills separately from our careers, disregarding years of experience and specialization, and even advanced degrees. A culture which perpetuates the dismissal of merit-based experience only seeks to systematically promote obedience and produce disciplined high-tech employees, jumping through arbitrary hoops at the whims of employers.

Perpetuating the Underrepresentation of Minorities

Robert Love, a Software Engineer at Google argues that in order to succeed in technical interviews, a candidate must “intuitively understand what the data structure looks like, what it feels like to use it, and how it is structured both in the abstract and physically in your computer’s memory.” Yet, it is never concretely defined what it means to “intuitively” know what a data structure “feels like”, while assuming that worthy candidates visualize and process knowledge in an equivalent manner, that is, there is only one inherent and “intuitive” understanding of computer science concepts.

Photo CC-BY Jared Tarbell, filtered.

Most concerningly, these justifications for technical interviews rely on an assumption of innate intuition or talent that a candidate possesses, a belief that might have a significant negative impact on tech culture and demographics. A recent study published in Science suggests that emphasis on having an “innate gift or talent” as a measure of success versus “motivation and sustained effort” may help explain the underrepresentation of women and African Americans in STEM. Measuring inherent intelligence, intuition or skills by means of the successful manipulation of algorithms is certainly a faulty measure of potential. For example, Etsy saw a “35% decline in gender diversity even when it was a priority” when they carried out technical interviews. However, by hiring women through a “Hacker School Hybrid”, Etsy grew their number of female engineers by almost 500% in one year.

Dismissing experience and “sustained effort” of candidates, while testing for nebulous “innate” abilities, thus contributes to the underrepresentation of minorities in tech and may even further the infamous imposter syndrome among them. It has been well-documented that impostor syndrome is more common among high-achieving women than men. To further disregard experience only promotes the imposter syndrome that many women experience: it entails that one’s experience was not a sufficient contribution in helping the candidate possess the necessary attributes for a given position. A Harvard Business Review study claimed that roughly 50% of women in science, engineering and technology will eventually leave due to the hostility of their fields. This hostility is often attributed to having to prove one’s self consistently, despite established accomplishment and success. A surprising justification given by proponents of technical interviews is thus paradoxical: they claim that to reduce “the extra fighting to prove we’re technical”, women must, ironically, further prepare and study to perform well in technical interviews. That is, our merit must be consistently proved by a set of technical questions aimed to measure an innate technical gift of some sort, despite other skills, experiences and expertise.

Towards a More Fair Interview Process

Photo CC-BY Jared Tarbell, filtered.

Improving the technical interview process to assure a fair evaluation to all is not a trivial task. Luckily, there are alternative models already in place that may be improved on. For example, Etsy partnered with Hacker School (now the Recurse Center), offering grants to women for a three-month, full-time school with the goal of recruiting more women to join engineering at Etsy and other tech companies. This allowed Etsy to evaluate their candidates and their work ethic through a hands-on experience developed over a period of time, closely mimicking the on-boarding of a new employee.

In order to sufficiently assess senior high-tech employees, we must construct an interview process which can incorporate and thus evaluate the experiences and specialities that they possess. A more effective process may involve an overhauled “take home coding exercise”. Traditionally, this strategy is not effective as it is merely used to screen if a candidate is worthy of the preliminary interview. Furthermore, the exercises are often generic and are not specific towards a niche skillset, and thus can be answered via a simple web-search. However, evolving this generic coding exercise to a tailored interview would allow the employer to evaluate more qualifications the candidate may claim. For example, an employer could construct an exercise that mimics a crucial task that had been carried out by employees of the same position; ideally, this task would exercise many qualities an employer seeks in a candidate without requiring prior knowledge of the company’s unique logistics.

Other interview strategies may include “dry runs” of candidates, similar to that of Etsy’s “Hybrid Hacker School”. That is, a candidate works on-site with a prospective team on a simulated task derived from an everyday scenario. This allows a team to understand the candidate’s abilities, teamwork, work ethic, and in-depth breadth of knowledge better than futile manipulation of a data structure would. Surprisingly, a day dry run would take no longer than a traditional Google interview, which often consists of 3 interviews of 45 minutes, lunch, and 2 more interviews.

It is conceivable that technical interviews may benefit recent CS graduates, as not only do they lack technical experience, but they would have likely taken courses which emphasized the intricate manipulation of data structures and algorithms fairly recently. However, it is unlikely that technical interviews are a fair method of evaluation which are benefitting other technical employees — particularly those from underrepresented groups — seeking positions relative to their specializations. If the worth of a prospective employee is measured by a set of technical questions that is far removed from their field of speciality, not only are employees doing candidates an injustice, but they may be depriving themselves from talent that cannot be measured in “innate” knowledge.