How to Interview Engineers

We do a lot of interviewing at Triplebyte. Indeed, over the last 2 years, I've interviewed just over 900 engineers. Whether this was a good use of my time can be debated! (I sometimes wake up in a cold sweat and doubt it.) But regardless, our goal is to improve how engineers are hired. To that end, we run background-blind interviews, looking at coding skills, not credentials or resumes. After an engineer passes our process, they go straight to the final interview at companies we work with (including Apple, Facebook, Dropbox and Stripe). We interview engineers without knowing their backgrounds, and then get to see how they do across multiple top tech companies. This gives us, I think, some of the best available data on interviewing.

In this blog post, I'm going to present what we've learned so far from this data. Technical interviewing is broken in a lot of ways. It's easy to say this. (And many blog posts do!) The hard part is coming up with what to do about it. My goal for this post is to take on that challenge, and lay out specific advice for hiring managers and CTOs. Interviewing is hard. But I think that many of the problems can be fixed by running a careful process [1].

The Status Quo

Most interview processes includes two main steps:
  1. Applicant screening
  2. In-person final interview
The goal of applicant screening is to filter out candidates early, and save engineering time in interviews. The screening process usually involves a recruiter scanning a candidate's resume (in about 10 seconds), followed by a 30-minute to 1-hour phone call. Eighteen percent of the companies we work with also use a take-home programming challenge (either in place of or in addition to the phone screen). Screening steps, interestingly, are where the significant majority of candidates are rejected. Indeed, across all the companies we work with, over 50% of candidates are rejected on the resume scan alone, and another 30% are rejected on on the phone screens / take-home. Screening is also where hiring can be at its most capricious. Recruiters are overwhelmed with volume, and need to make snap decisions. This is where credentials and pattern matching come into play.

In-person final interviews almost-universally consist of a series of 45-minute to 1-hour sessions, each with a different interviewer. The sessions are primarily technical (with one or two at each company focusing on culture fit and soft skills). The final hire/no hire decisions are made in a decision meeting after the candidate has left, with the hiring manager and everyone who interviewed the candidate. Essentially, a candidate needs at least one strong advocate and no strong detractors to be made an offer [2].

Beyond the common format, however, final interviews vary widely.
  • 39% of the companies we work with run interviews with a marker on a whiteboard
  • 52% allow the candidate to use their own computer (the remaining 9% are inconsistent)
  • 55% let interviewers pick their own questions (the remaining 45% use a standard bank of questions)
  • 40% need to see academic CS skills in a candidate to make an offer
  • 15% dislike academic CS (and think that talking about CS is a sign that a candidate will not be productive)
  • 80% let candidates use any language in the interview (the remaining 20% require a specific language)
  • 5% explicitly evaluate language minutia during the interview
Across all the companies we work with, 22% of final interviews result in a job offer. (This figure comes from asking companies about their internal candidate pipeline. Candidates applying through Triplebyte get offers after 53% of their interviews.) About 65% of offers are accepted (result in a hire). After 1 year, companies are very happy with approximately 30% of hires, and have fired about 5% [3].

False Negatives vs. False Positives

So, what's wrong with the status quo? Fire rates, after all, don't seem to be out of control. To see the problem, consider that there are two ways an interview can fail. An interview can result in a bad engineer being hired and later fired (a false positive). And an interview can disqualify someone who could have done that job well (a false negatives). Bad hires are very visible, and expensive to a company (in salary, management cost and morale for the entire team). A bad hire sucks the energy from a team. Candidates who could have done the job well but are not given the chance, in contrast, are invisible. Any one case is always debatable. Because of this asymmetry, companies heavily bias their interviews toward rejection.

This effect is strengthened by noise in the process. Judging programming skill in 1 hour is just fundamentally hard. Add to this a dose of pattern matching and a few gut calls as well as the complex soup of company preferences discussed above, and you're left with a very noisy signal.

In order to keep the false positive rate low in the face of this noise, companies have to bias decision ever farther toward rejection. The result is a process that misses good engineers, still often preferences credentials over real skill, and often feels capricious and frustrating to the people involved. If everyone at your company had to re-interview for their current jobs, what percentage would pass? This is a scary question. The answer is almost certainly well under 100%. Candidates are harmed when they are rejected by companies they could have done great work for, and companies are harmed when they can't find the talent they need.

To be clear, I am not saying the companies should lower the bar in interviews. Rejection is the point of interviewing! I'm not even saying that companies are wrong to fear false positives far more than false negatives. Bad hires are expensive. I am arguing that a noisy signal paired with the need to avoid bad hires results in a really high false negative rate, and this harms people. The solution is to improve the signal.

Concrete ways to reduce noise in interviews

1. Decide what skills you're looking for

There is not a single set of skills that define a good programer. Rather, there is a sea of diverse skill sets. No engineer can be strong in all off these areas. In fact, at Triplebyte we often see excellent, successful software engineers with entirely disjoint sets of skills. The first step to running a good interview, then, is deciding what skills matter for the role. I recommend you ask yourself the following questions (these are questions we ask when we onboard a new company at Triplebyte).
  • Do you need fast, iterative programmers, or careful rigorous programmers?
  • Do you want someone motivated by solving technical problems, or building product?
  • Do you need skill with a particular technology, or can a smart programmer learn it on the job?
  • Is academic CS / math / algorithm ability important or irrelevant?
  • Is understanding concurrency / the C memory model / HTTP important?
There are no right answers to these questions. We work with successful companies that come down on both sides of each one. But what is key is making an intentional choice, based on your needs. The anti-pattern to avoid is simply picking interview questions randomly (or letting each interviewer decide). When that happens, company engineering culture can skew in a direction where more and more engineers have a particular skill or approach that may not really be important for the company, and engineers without this skill (but other important skills) are rejected.

2. Ask questions as close as possible to real work

Professional programmers are hired to solve large, sprawling problems over weeks and months. But interviewers don't have weeks or months to evaluate candidates. Each interviewer typically has 1 hour. So instead, interviewers look at a candidates' ability to solve small problems quickly, while under duress. This is a different skill. It is correlated (interviews are not completely random). But it's not perfectly correlated. Minimizing this difference is the goal when developing interview questions.

This is achieved by making interview question as similar as possible to the job you want the candidate to do (or to the skill you're trying to measure). For examples, if what you care about is back-end programming, asking the candidate to build a simple API endpoint and then add features is almost certainly a better question than asking them to solve a BFS word chain problem. If you care about algorithm ability, asking the candidate to apply algorithms to a problem (say, build a simple search index, perhaps backed by a BST and a hashmap for improved deletion performance) is almost certainly a better problem than asking them to determine if a point is contained in a concave polygon. And a debugging challenge, where the candidate works in a real codebase, is almost certainly better than asking the candidate to solve a small problem on a whiteboard.

That said, there is an argument for doing interviews on whiteboards. As an interviewer, I don't care if an engineer has the Python itertools module memorized. I care if they can think through how to use iterators to solve a problem. By having the candidate work on a whiteboard, I free them from having to get the exact syntax right, and let them focus on the logic. Ultimately I think this argument fails, because there's just not enough justification for the different format. You can get all the benefit by allowing the candidate to work on a computer, but telling them their code does not need to run (or even better, making it an open book interview and letting them look up anything they want with Google).

There is an important caveat to the idea that interview questions should mirror work. It is important that an interview question be free from external dependencies. For example, asking a candidate to write a simple web scraper in Ruby might seem like a good real-word problem. However, if a candidate needs to install Nokogiri (a Ruby parsing library that can be a pain to install) and they end up burning 30 minutes wrestling with the native extensions, this becomes a horrible interview. Not only has time been wasted, stress for the candidate has gone through the roof.

3. Ask multi-part questions that can't be given away

Another good rule of thumb for interview questions is to avoid questions that can be “given away”, i.e. avoid questions where there's some magic piece of information that the candidate could have read on Glassdoor ahead of time that would allow them to answer easily. This obviously rules out brain teasers or any question requiring a leap of insight. But it goes beyond that, and means that questions need to be a series of steps that build on each other, not a single central problem. Another useful way to think about this is to ask your self whether you can help a candidate who gets stuck, and still end the interview with a positive impression. On a one-step question, if you have to give the candidate significant help, they fail. On a multi-part problem, you can help with one step, and the candidate can then ace everything else and do well.

This is important not only because your question will leak onto Glassdoor, but also (and more importantly) because multi-part problems are less noisy. Good candidates will become stressed and get stuck. Being able to help them and see them recover is important. There is significant noise in how well a candidate solves any one nugget of programming logic, based on whether they've seen a similar problem recently, and probably just chance. Multi-part problems smooth out some of that noise. They also give candidates the opportunity to see their effort snowball. Effort applied to one step often helps them solve a subsequent step. This is an important dynamic when doing real work, and capturing it in an interview decreases noise.

To give examples, asking a candidate to implement the game Connect Four in a terminal (a series of multiple steps) is probably a better question than asking a candidate to rotate a matrix (a single step, with some easy giveaways). And implementing k-means clustering (multiple operations that build on each other) is probably better than determining the largest retangle that can fit under a histogram.

4. Avoid hard questions

If a candidate solves a really hard question well, that tells you a lot about their skill. However, because the question is hard, most candidates will fail to solve it well. The expected amount of information gained from a question, then, is heavily impacted by the difficulty of the question. We find that the optimal difficulty level is significantly easier than most interviewers guess.

This effect is amplified by the fact that there are two sources of signal when interviewing a candidate: whether they give the “correct” answer to a question, and their process / how easily they arrive at that answer. We've gathered data on this at Triplebyte (scoring questions both on whether the candidate reached the correct answer, and how much effort it took them, and then measuring which scores predict success at companies). What we found is a tradeoff. For harder questions, whether the candidate answers correctly carries most the signal. For easier questions, in contrast, most of the signal is found in the candidate's process and how much they struggle. Considering both sources of signal, the sweet spot is toward the easier end of the spectrum.

The rule of thumb we now follow is that interviewers should be able to solve a problem in 25% of the time they expect candidates to spend. So, if I'm developing a new question for a 1-hour interview, I want my co-workers (with no warning) to be able to answer the question in 15 minutes. Paired with the fact that we use multi-part real-world problems, this means that the optimal interview question is really pretty straightforward and easy.

To be clear, I am not arguing for lowering the bar in terms of pass rate. I am arguing to ask easy questions, and then including in your evaluation how easily the candidate answered the questions. I'm arguing for asking easy questions, but then judging fairly harshly. This is what we find optimizes signal. It has the additional benefit of being lower stress for most applicants.

To give examples, asking a candidate to create a simple command line interface with commands to store and retrieve key-value pairs (and adding functionality if they do well) is probably a better problem than asking a candidate to implement a parser for arithmetic expressions. And a question involving the most common data structures (lists, hashes, maybe trees) is probably better than a question about skiplists, treaps or other more obscure data structures.

5. Ask every candidate the same questions

Interviews are about comparing candidates. The goal is to sort candidates into those who can contribute well to the company and those who can't (and in the case of hiring for a single position, select the best person who applies). Given this, there is no justification for asking different questions to different candidates. If you evaluate different candidates for the same job in different ways, you are introducing noise.

The reason it continues to be common to select questions in an ad-hoc fashion, I think, is because it's what interviewers prefer. The engineers at tech companies typically don't like interviewing. It's something they do sporadically, and it takes them away from their primary focus. In order to standardize the questions asked to every candidate, the interviewers would need to take more time to learn the questions and talk about scoring and delivery. And they would need to re-do this every time the question changed. Also, always asking the same question is just a little more tedious.

Unfortunately, the only answer here is for the interviewers to put in the effort. Consistency is key to running good interviews, and that means asking every candidates the same questions, and standardizing delivery. There's simply no alternative.

6. Consider running multiple tracks

In conflict with my previous point, consider offering several completely different versions of your interview. The first step when designing an interview is to think about what skills matter. However, some of the answers might be in conflict! It's pretty normal, for example, to want some really mathy engineers, and some very productive / iterative engineers (maybe even for the same role). In this case, consider offering multiple versions of the interview. They key point is that you need to be at enough scale that you can fully standardize each of the tracks. This is what we do at Triplebyte. What we've found is that you can simply ask each candidate which type of interview they'd prefer.

7. Don't let yourself be biased by credentials

Credentials are not meaningless. Engineers who have graduated from MIT or Stanford, or worked at Google and Apple really are better, as a group, than engineers who did not. The problem is that the vast majority of engineers (myself included) have done neither of these things. So if a company relies on these signals too heavily, they will miss the majority of skilled applicants. Giving credentials some weight in a screening step is not totally irrational. We don't do this at Triplebyte (we do all of our evaluation 100% background blind). But giving some weight to credentials when screening might make sense.

Letting credentials sway final interview decision, however, does not make sense. And we have data showing that this happens. For a given level of performance on our background-blind process, candidates with a degree from a top school go on to pass their interviews at companies at a 30% higher rate than candidates without the name-brand resume. If interviewers know that candidate has a degree from MIT, they are more willing to forgive rough spots in the interview.

This is noise, and you should avoid it. The most obvious way is just to strip school and company names from resumes before giving them to your interviewers. Some candidates may mention their school or company, but we do all our interviews without knowing the candidates' backgrounds, and it's actually pretty rare for a candidate to bring it up during technical evaluation.

8. Avoid hazing

One of the ugliest ways interview can fail is that they can take on an aspect of hazing. They're not just about evaluating the skill of a candidate, they're also about a group or team admitting a member. In that second capacity, they can become a rite of passage. Yes, the interview is stressful and horrible, but we all did it so so should the candidates. This can be accentuated when a candidate is doing badly. As an interviewer, it can be frustrating to watch a candidate beat their head against a problem, when the answer seems so obvious! You can get short tempered and frustrated. This, of course, only increases the stress for the applicant in a downward spiral.

This is something you want to stay a mile away from. The solution is talking about the issue and training the interviewers. One trick that we use is, when a candidate is doing really poorly, to switch from evaluation mode, where the goal is to judge the candidate, to teaching mode, where the goal is to make the candidate understand the answer to the question. Mentally making the switch can help a lot. When you're in teaching mode, there no reason to withhold information or be anything other than friendly.

9. Make decisions based on max skill, not average or min skill

So far, I've only talked about individual questions, not the final interview decision. My advice here is to try to base the decision on the maximum level of skill that the candidate shows (across the skill areas you care about), not the average level or minimum level.

This is likely what you are already doing, intentionally or not! The way hire/no hire decisions are made is that everyone who interviewed a candidate gets together in a meeting, and an offer is made if at least one person is strongly in favor of hiring, and no one is strongly against. To get one interviewer to be strongly in favor, what a candidate needs to do is ace one section of the interview. Across our data, max skill is the attribute that's most correlated with acing at least one section of a company's interview. However, to be made an offer, a candidate also needs no one to be a strong no against them. Strong noes come when a candidate looks really stupid on a question.

Here we find just a great deal of noise. There are so many different ways to be a skilled engineer, that almost no candidates can master them all. This means if you ask the right (or wrong) question, any engineer can look stupid. Candidates get offers, then, when at least one interview lines up with an area of strength (max skill) and no areas line up with a significant weakness. The problem is that this is noisy. The same engineer who fails one interview because they looked stupid on a question about networking passes other interviews with flying colors because that topic did not come up.

The best solution, I think, is for companies to focus on max skill, and be a little more comfortable making offers to people who looked bad on parts of the interview. This is, looking for strong reasons to say yes, and not worrying so much about technical areas where the candidate was weak. I don't want to be absolute about this. There are of course technical areas that just matter to a company. And deciding that you want to have a culture where everyone on the team is at a certain level in a certain area may well make sense. But focusing more on max skill does reduce interview noise.

Why do interviews at all?

A final question I should answer is why do interviews at all? I'm sure some readers have been gritting their teeth, and saying “why think so much about a broken system? Just use take-home projects! Or just use trial employment!” After all, some very successful companies use trial employment (where a candidate joins the team for a week), or totally replace in-person interviews with take-home projects. Trial employment makes a lot of sense. Spending a week working beside an engineer (or seeing how they complete a substantial project) almost certainly provides a better measure of their abilities than watching them solve interview problems for 1 hour. However, there are two problems that keep trial employment from replacing standard interviews:
  1. Trial employment is expensive for the company. No company can spend a full week with every person who applies. To decide who makes it to the trial, companies must use some other interview process.
  2. Trial employment (and large take-home projects) are expensive for the candidate. Even when they are paid, not all candidates have the time. An engineer working a full-time job, for example, may simply not be able to take the time off. And even if they can, many won't. If an engineer already has job offers in hand, they are less likely be willing to take on the uncertainty of a work trial. We see this clearly among Triplebyte candidates. Many of the best candidates (with other offers in hand) will simply not do large projects or work trials.
The result of this that trial employment is an excellent option to offer some candidates. I think if you have the scale to support multiple tracks, adding a trial employment track is a great idea. However, it's not viable as a total replacement for interviews.

Talking to candidates about past experience is also sometimes put forward as a replacement for technical interviews. To see if a candidate can do good work in the future, the logic goes, just see what they've done in the past. We've tested this at Triplebyte, and unfortunately we've not had great results. Communication ability (ability to sell yourself) ended up being a stronger signal than technical ability. It's just too common to find well-spoken people who exaggerate their role (take credit for a team's work), and modest people who downplay what they did. Given enough time and enough questioning, it should be possible to get to the bottom of this. However, we found that within the time limits of a regular interview, talking about past experience is not a general replacement for interviewing. It is a great way to break the ice with a candidate and get a sense of their interests (and judge communication ability and perhaps culture fit). But it's not a viable total replacement for interviews

Good things about programming interviews!

I want to end up this post on a more positive note. For everything that's wrong with interviews, there is a lot that's right about them.

Interviews are direct skill assessment. I have friends who are teachers, who tell me that teacher interviews are basically a measure of communication ability (ability to sell yourself), and a credential. This seems to be true of many many professions. Silicon Valley is not a perfect meritocracy. But we do at least try to directly measure the skills that matter, and stay open to the idea anyone with those skills, regardless of background, can be a great engineer. Credential bias often stands in the way of this. But we've been able to mostly overcome this at Triplebyte, and help a lot of people with unconventional backgrounds get great tech jobs. I don't think Triplebyte would be possible, for example, in the legal field. The reliance on credentials is just too high.

Programmers also choose interviews. While this is a very controversial topic (there are certainly programmers who feel differently), when we've run experiments offering different types of evaluation, we find that most programmer still pick a regular interview. And we find that only a minority of programmers are interested in companies that use trial employment or take-home projects. For better or worse, programming interviews seem to be here to say. Other types of evaluation are great supplements, but they seem unlikely to replace interviews as the primary way engineers are evaluated. To misquote Churchill, “Interviews are the worst way to evaluate engineers, except for all the other ways that have been tried from time to time.”


Interviewing is hard. Human beings are hopelessly complex. On some level, judging human ability in a 4-hour interview is just a fool's errand. I think it's important to stay humble about this. Any interview process is bound to fail a lot of the time. People are just too complex.

But that's not an argument for giving up. Trying to run a meritocratic process is better than not trying. At Triplebyte, our interview is our product. We brainstorm ideas, we test them, and we improve over time. This, I think, is the approach that's needed to improve how engineers are hired. In this post, I've shared some of the big things we've learned over the last two years. I'd love to get feedback, and hear if these ideas are helpful for people. Send me an email at

If you're a company looking for engineers, we'd also love to help you hire. You can send me an email, or check out our companies page.

Thanks to Adora Cheung and Jared Friedman for reading earlier drafts of this post.

[1] I'm limiting this blog post to technical skill assessment. I'll be writing a future post about culture fit, behavioral interviews and non-technical evaluation.

[2] There is of course variation here. At opposite ends of the spectrum we see companies that require a unanimous yes from every interviewer to make a hire, and companies where the hiring manager is solely responsible for the decision.

[3] These numbers are what companies report about their internal candidates. And the numbers vary widely between companies (they report fire rates, for example, as low as 1% and as high as 30%). The numbers are significantly better for Triplebyte candidates. So far, our candidates at companies have received offers after 53% of interviews, and 2% have been fired.

25 responses
I find the ending of this article confusing. You admit that technical interviews are flawed, and mention two alternatives: trial employment, and take-home projects. Then you list a couple problems with trial employment. Then you list some problems with "talking to candidates" as another interview alternative. Every other possible method (including those used successfully in other industries) are discounted entirely. I wonder, in a world where we had started with trial employment, or some other method, if technical interviews would be seen as "not a viable as a total replacement" [sic]. It's not at all obvious to me that the flaws with trial employment are any worse than the flaws with technical interviews. You don't even touch on some of the worst problems with interviews, like subtle but present sexism and racism and ageism. You observe that "Many of the best candidates (with other offers in hand) will simply not do large projects or work trials", but how many candidates are avoiding you because they know you do traditional technical interviews? (Me, for one. I've passed on many companies for this reason.) Do you think you can get away with it simply because most of your competitors are? You're never going to hire top talent that way, anyway.
Alex, thanks for pointing out the extraneous word. Fixed! We've done both traditional-format interviews, and take-home project interviews. When offering both, we found that under 20% of applicants were interested in the take-home projects. We also have the data source of seeing how candidate who go through our process reason about what companies to interview with. We find that most are negative about trial periods (they'd rather just get an offer!) Another data source is engineer responses on HN. I think you'll note that whenever trial employment or large take-home projects come up, there is a significant number of negative responses (it's controversial). You are correct that we're blind to candidates that don't apply to us and that that could bias our results. But because of our experiments with take-home projects and the other things I mentioned, I'm pretty sure that my statement that the majority of engineer prefer a single day of interviewing is true. I'm all for trial employment as an option. But I don't think it's a replacement.
I think it is not a problem with takehome projects per se. It is a problem with the way currently companies build and use takehome projects. They don't put any effort in making it short and to the point, at times it feels like they are looking for free work, you end up doing similar takehomes over and over again for multiple companies, etc. I come from a data science perspective where takehomes are maybe more needed than in pure software engineering, but I think everyone would love a clear and targeted takehome challenge that takes say max 4 hrs. And then that's used by several companies at the same time. Also, as the guy was saying above, it is very hard to predict what would happen with a change in interview formats. Many people are really good at their job, but because interviews are based on useless algos/tricks/theory, they choose to not interview. And often end up changing job by following their previous boss or leveraging their network in general. You guys are trying to solve the problem: out of people who interview today, how do I find the best ones? I think a more useful question would be: how do I get the best people to interview? That would eventually lead to much better hires.
I agree with much of this. Some additional comments: > "2. Ask questions as close as possible to real work: This is achieved by making interview question as similar as possible to the job you want the candidate to do (or to the skill you're trying to measure)." This "or" thing in parentheses is really important. Interviews should not be "as similar as possible to the job." They should be all about the skill you want to measure. And there's quite a difference between these. People can be trained, assuming you have time and resources. The point of standard algorithm interviews is to measure a particular skill: intelligence. The belief behind this is smart people will tend to solve problems well in the real world. It's okay if they don't know all the skills they need; they can learn them. (And if there's a particularly challenging skill, then maybe you need to add that into the process.) > "4. Avoid hard questions: If a candidate solves a really hard question well, that tells you a lot about their skill. However, because the question is hard, most candidates will fail to solve it well. The expected amount of information gained from a question, then, is heavily impacted by the difficulty of the question. We find that the optimal difficulty level is significantly easier than most interviewers guess. This effect is amplified by the fact that there are two sources of signal when interviewing a candidate: whether they give the “correct” answer to a question, and their process / how easily they arrive at that answer." You don't give examples of what qualifies as hard vs. easy, so it's a little difficult to judge this. But generally, I'd advocate for people asking harder questions. When a question is easy, a little thing -- small point of confusion, etc -- can make a big difference in whether the candidate seems to have gotten to that answer well or not. When a question is hard, this is when you can actually see a great differential between great vs. good vs. okay. You can offer help and see how effectively they pick up on that advice. If your interviewers are just throwing out a question and then sitting back and seeing what happens, then yeah, candidates won't be able to tackle a hard question. But that's not what they should be doing. To be clear here: hard does not mean "ask about some obscure data structure [skip lists, etc]." It means a challenging question involving common knowledge (hash tables, strings, arrays, maybe binary search trees). > 5. Ask every candidate the same questions: [...] there is no justification for asking different questions to different candidates. If you evaluate different candidates for the same job in different ways, you are introducing noise. I think this is overplayed a little. If you're doing algorithm-style interviews, any question should be basically evaluating the same skill (problem solving skills). If I ask my favorite question and you ask your favorite question, but they're evaluating the same skill, then it doesn't really matter. There's nothing to make one question better than another. Why not let each interviewer ask the question that they like? Now, if every interviewer asks a different question, you won't be able to give a well-defined metric on what good performance looks like for that problem. But that's okay. You can't really do that anyway. When companies give well-defined metrics, they get too rigid. The candidate takes slightly longer to complete the code because the candidate thought things through more thoroughly, and now they're pushed into a lower performing bucket. And that's wrong. These standardized metrics sound good in theory, but they don't work well in reality (for algorithm interviews, anyway).
I interviewed with Triplebyte, or did I? You asked me to interview, you sent me to an online test that asked questions that were both CS oriented as well as very domain specific (CSS and web stack) questions that few people outside the web application domain would care about. The online screener told me I had done well. The next day you sent me an email saying I had not done well enough. (Literally three days later another website sent me to a similar online test with almost identical questions) So I am going to be bold here and say your results were noisy and your online screener mostly meaningless. I am also going to suggest an alternative to flunking the person who does badly on one section is either to throw out a bad section OR give the person a redo, where that redo begins with a frank discussion of what was going on, and a try with a completely different question. Also, the better test that you don't bring down is the open book test with one of the company's APIs. Here Joe: take two hours, by yourself, I'll be available by chat or email, use google, and build an X out of our Y API, or add Z to it.
I took the test with you guys cause I liked how you described your company. However, I was hugely disappointed. It was some algorithm totally unrelated to work. It was something like given a bunch of numbers, find all possible ways to achieve a given target. I mean, that's like those complicated algorithms that Google or MS would ask 10 or 15 years ago. All the marketing about creating a new way to interview engineers is great. But maybe you should also actually do that?
The interview process is broken and you're just a reverberation of it as you sub-hire for people with broken interview processes.
Have to agree with anon above. I signed up with TripleByte late last year and was given the online test and then a coding question to solve in an hour(?). I didn't do well on the question and so was rejected the next day. Later I interviewed with Facebook. I messed up the first phone screen but was given a second phone screen. Did well on that and was pretty much perfect in the onsite round. Went onsite at Google and was again perfect in all three coding rounds and one of two system design rounds. I ended up getting offers from both (you should be able to figure that out from the IP address of this comment). >> This means if you ask the right (or wrong) question, any engineer can look stupid. So how does your process consist of only one screening question to decide if you should proceed with a candidate. I am not sure if you have changed the process since late last year but if you haven't then I am not sure what you have learned from this blog post.
I definitely agree with you on trial employment. Look here's the deal. It's EXTREMELY expensive to have intense technical interviews that are definitely flawed. They are simply torture tests really and a LOT of good candidates get missed. It's also very time intensive for both parties. How many jobs do you think someone can apply to when each one requires 8hrs of investment? What if they are trying to switch jobs? So they're working full-time somewhere else? Come on. It isn't that hard. ...But that's why everyone says it's hard to find good talent...Because the way in which they are looking and interviewing is fundamentally broken. What? You think with the number of resumes floating by an employer's desk, they're all just bad? You know statistically speaking that sounds silly, right? No, most people just suck at weeding out the people to go interview and then suck even more at interviewing. Ok. So trial employment (once you narrow down who should get a trial) is great for the technical. It puts talent in direct contact with real world code - not some take home assignment that isn't relevant. I'll give you an example here too -- I once wrote a path finding algorithm as a take home assignment. In the interview I asked if they wrote games. They said no. I was very shocked and they kinda laughed knowing their test was flawed, but they were happy that I could figure it out. BUT that didn't mean that I was going to be a good employee/asset for them. So what did they do? They wasted my time building it and theirs reviewing it. That flawed interview process? Yea, that costs the company a LOT of money over time. Not just in doing the process but also in hiring the wrong candidates because of the flawed process to only then have to go and find new talent. Now, the real kicker here is that almost no interview process can figure out if someone is going to be a good fit culturally. It's simply not enough time to be spending with someone. So the trial employment (and many, many, companies are "at will" anyway, so it's always a trial as far as I'm concerned) is a very good solution. But how do you decide who to give a chance? Well, the things I look for when interviewing is how motivated a person is. How current is their knowledge? How engaged are they? Are they excited about what they do? Heck, are they excited about the interview? I look for passion and energy. I look for a basic level of understanding of the tasks at hand and then combine that with their years of experience. I also look at their willingness to go freaking Google something. I LOVE when people say that they don't know, but will look it up or would look it up. I get a HARD ON when they say that they are going to go look it up after the interview because it interests them and they just learned about something new in the interview. THAT is how everyone should be interviewing. If you aren't interviewing with an HR violation, you aren't interviewing properly. =) (a joke for my exaggeration that hopefully everyone got) At the end of the day, programming is WAY too vast to ever truly expect someone to master it all. We all have things we like which perpetuate our strengths and weaknesses. The important thing is that someone is capable. So I'll leave you with this: capability and ability are two very important things to discern when hiring an engineer.
Great article! I would argue that whiteboarding or algorithmic questions don't necessarily aid in finding an ideal candidate. Some exceptional candidates don't do well at these scenarios, but do quite well in conversational exploration. I know the Google process is optimized for efficiency, but most companies don't have that requirement and would do best to tailor the interview to the candidate. Canned questions and CS algorithmic questions seem to test the interview skills of the candidate and not the essential qualities of them, leading to false negatives. I feel that I can tell if the person is a "good fit" within 5 minutes of an interview without any "classic" questions.
Thanks a lot for this article which will be very helpful to all engineers out there to understand the process and on similar note you can also check our page too which will help job seekers and graduates. Thanks & reagrds, Rahul Ekbote
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