Startup Hiring

Hiring is an area which is usually given a lot of lip service but neglected in practice. Especially in a startup, where there is a shortage of workforce, hiring at all levels has a profound impact on the future of the organization. When compared to a big organization, the value proposition of a startup to its employees is concerning speed, freedom, and responsibility. But, when it comes to hiring, startups adopt the cookie cutter hiring strategy of big companies.

The regular interview process is biased toward extroverts and people who can speak well. In my opinion and observation, this does not correlate to someone who is good at what she does. I have been observing this since my college days. During campus placements, flamboyant and boisterous students made their way into companies while the quiet ones who were good at studies and who could get along with others were often overlooked. The same thing happens to a lesser extent with the regular interview process too.

Taleb says the following of doers.

For those who do things, it is harder to talk about what they do. Reality doesn’t care about talk. It is full of pauses and hesitations. Clear, non-hesitant speech is the domain of non-doers.

Interviewing is an intimidating experience, and not a lot of people excel in this, especially people who are good at what they do. Also, the kind of questions that are asked during an interview has hardly any resemblance to the day to day work. I have written about this before too. A big step in this direction is to craft an interview process that mimics your organization’s day to day tasks. The best way to do this is to have an assignment based interview process. You can create assignments that resemble tasks that you do on a daily basis and ask candidates to work their way through this. An approach of this sort takes the whole adversarial, and interrogative tone out of interviews and makes it an enjoyable experience.

An important aspect to take care while creating an interview process is to take subjectivity out of interviews. Who conducts the interview should not have a bearing on the outcome of the interview. Another thing to keep in mind is to subject all candidates of a particular level to the same questions. Following the above ensures that you are evaluating every candidate on the same yardstick. It is essential to do this to create a quantitative interview process. This sort of standardization also helps in creating a checklist of what you expect in answers to questions. If you have these covered, anyone can take anyone’s interview and evaluate candidates. Is it not ironic that startups claim to be data-driven, but when it comes to interviews, the whole data-driven approach takes a corner seat.


An example interview template would be to have an assignment that closely resembles a task related to work. Try to keep the assignment practical yet straightforward. A good candidate should not take more than 3 to 4 hours to complete the assignment. On submission; evaluate the assignment on code quality, conventions, comments, instructions to run, test cases, boundary condition handling, error handling, and documentation. Post that, have a phone screen with a bunch of questions that mostly have a one-word answer. Make this as practical and broad as possible. Try to touch all aspects of work from data structure choices to computational complexity to databases, web servers, etc. The idea here is that if someone is good at what they do, they should be able to answer these without any preparation. Then invite the candidate for face to face round where you ask the candidate to expand on the take-home assignment. Add a bit more complexity and see how the candidate fares. Finish off the entire process with a design round. I have presented a very rough template, tweak it, add more rounds and improvise to fit your needs; every organization is different.

Along with all the above, soft skills are critical. The sad fact is a lot of people do not know how to conduct interviews. I have heard of instances where interviewers were rude and outright obnoxious, not punctual, candidates not kept informed in advance of the interview schedule, candidates not being adequately attended during the face to face rounds, etc. All these play a significant role in shaping the image of the organization in the candidate’s mind.

In the talent market, big companies, as well as startups, are competing for the same piece of the pie. By adopting a hiring process similar to big companies, startups are not going to get an ace up their shoulder. What startups should be focussing on is coming up with a flexible hiring process that is unique to their organization. It is not easy to execute this in a big corporation but very much doable in a startup. Starting from how you engage with a candidate for the first time to the interview to the offer rollout process, everything is ripe for disruption. Differentiate yourself from others. An interview is a big opportunity for you to make a mark on the candidate, seize it right there.

The Algorithm and Data Structures Conundrum

Hacker news denizens have a penchant for articles on hiring. Every once in a while, a post appears on the home page followed by a passionate and vituperative debate. People conversing on this can be divided into two camps; one argues that math, algorithm and data structures are critical and interview candidates should be quizzed on them while the other group takes the opposite view. If you are not privy to these conversations, I highly encourage you to go through them.

In my opinion, these discussions miss the real essence of the issue. The question is not whether algorithms and data structures are a requirement for a programmer, it goes without saying it is. What we should debate is the depth of knowledge someone should have on these to be effective at their day to day work. Are we after someone who knows the different sorting algorithms and has a fair understanding of their trade-offs? At work, if this person faces a tricky problem, she knows where to start and then reaches out to Google to aid her? Or, do we want someone who can go to the whiteboard and code the algorithm from scratch and explain the math and computational complexity right in the interview? I can very confidently say that for most of the day to day work, you do not need to ponder on the mathematics behind algorithms and complex data structures. What is required is sound coding and design skills, tenacious curiosity, and a passion for excelling.


What do I mean when I say sound coding skills? The very basics; splitting lengthy code into modules, appropriate comments in the code, test cases, following code conventions and the spirit of the programming language. The above is the foundation of a great project and has a tremendous impact on the future of the project. If you do not follow these basic tenets of programming, scaling the project and modifying it in future becomes a project of its own.

Good design is what helps you scale your application as the business needs change and user base expands. In a web project, a good design would be to have business logic in a separate layer and not tying it up with controllers and views. I have personally seen this helping us at burrp! where we rolled out our mobile app in a month as our backend was flexible enough to support a new interface with minimal changes. If our design was not good, there was no way we could have developed a mobile app which had most of the functionalities of our website in such a short period.

Now coming to the most critical part, curiosity. Why is curiosity such an important trait? Without curiosity, we stagnate. We become complacent. We do not innovate. We do not try to become incrementally better. We think what we know is the ultimate truth and whatever language/framework/methodology that we use is the best. I have interacted with professional programmers who were not aware of PHP which powers a substantial number of websites.

If you are tenaciously curious, when faced with a problem, you start obsessing about it. You see it as a puzzle that has to be solved. For example, if you feel that a part of your code is slow, you dig deep into it and try to figure out what is happening. In this process, you come across people who have faced similar issues and solved the problem using a better algorithm or data structure.

I would say interviews focused heavily on data structures and algorithms are biased towards new college students and people who are just preparing for interviews. Most of these questions are in no way novel. Go to one of the interview preparation sites, spend a couple of days trying to solve the problems and suddenly you become an algorithm and data structures expert. Just because FANGs ask these kinds of questions in their interviews, everyone has adopted this interviewing style, and it has got to a point where interviews are more about showing off the interviewer’s data structure and algorithm skills rather than evaluating whether the person on the other side of the table is a good fit for the job. Also, an unhealthy side effect of this is that in a fury to concentrate on algorithms and data structure skills, everything else gets sidelined.

Unless you are working on some cutting-edge niche project, you do not need people who can whiteboard algorithm and data structure problems in interviews. Hiring people who are infinitely curious and can get incrementally better will give you a much better bang for your buck.