7 ms·

Bayesian Inference for Hiring Engineers

50 points by Harj 8y ago

deleted 8y ago
[deleted]
closed 8y ago
Edit: I think I've run afoul of an anti-triplebyte sentiment. I should clarify that I think this post did a good job building a very simple example of the statistical theory behind assessment,but I have no idea whether their product / approach is reasonable or not. Building a good assessment is much more than just statistics, and it sounds from other comments that there are serious concerns about the validity of their tool. Really enjoyed the build up from simple cases, to more complex models! If you're interested in the statistics behind estimating skill, and how well questions tell apart novices from experts, check out item response theory :) https://en.m.wikipedia.org/wiki/Item_response_theory https://en.m.wikipedia.org/wiki/Item_response_theory
compumike 8y ago
Companies seem to be adding more screening steps to try to reduce their false positive rate -- the rate at which they interview people who aren't hireable. But most don't seem to understand that mathematically, there's a tradeoff for a higher false negative rate. Screening false negatives are people who would have done well in an interview, but don't make it to that stage. These are more hidden to the company, but quite expensive for the hiring process, and painful for people who are wrongly rejected. If we put this in probabilistic terms, I hope we can have a deeper conversation about what's happening and how this issue impacts engineers.
- tmpz22 8y ago
  What I'm worried about is the b2b crowdsourcing of these rankings between companies. Corporations have shown an affinity for sharing data, like Facebook and Google buying ad targeting data. I feel like it is only a matter of time before your tinder data, resume data, etc., is all fed into your credit score, etc., to the point where a minority of the population is completely screwed over by false negative rates made by bad data scientists.
  - sjg007 8y ago
    Any type of score is regulated by the Fair Credit and reporting act. These scores may fall under that regulation, especially if they are operating as a clearinghouse.
    - CoolGuySteve 8y ago
      That's true for your credit score, but these screenings could effectively blacklist someone from the industry if a bad encounter with TripleByte or someone like them is shared between all employers.
      - sjg007 8y ago
        Still relevant and still covered.
  - itronitron 8y ago
    I wouldn't be surprised if TripleByte has already triangulated data with HN or other YC companies to get additional profile information on applicants. The unpredictability of what will show up on the tests, and the unknown future of my 'grade', is a major turn-off. I'd prefer to see a certification process, where you can study for the test, then take the test, and pay to retake it if you want to improve your score. If the technical knowledge in the tests is that important for their clients, then sharing that openly will increase the pool of successful candidates over the long term.
- n42 8y ago
  At least where I work, we are painfully aware of this tradeoff, but are willing to make the sacrifice because of the severe costs of making a bad hire. We are small, and cannot afford to lose 90 days. I realize this article refutes this point of view, but finance and HR do not see it this way. I am more happy to devote several hours of each week interviewing than entire days/weeks mentoring and reviewing code for a bad hire, or worse, dealing with the widespread consequences of a culture misfit in a small team. Hiring is hard.
  - compumike 8y ago
    (Original article author here.) I'd distinguish between screening false positives (which my article focuses on) and hiring false positives. Screening rejections (before the final interview) are usually done with very limited information, so there's more room for bias and noise. Hiring is hard! :)
    - n42 8y ago
      You're right, the difference is important. I've been the tail end of the interview funnel when experimenting with our screening strictness. The emotional toll that less stringent screening had on my day-to-day work (faster, rapid, and high frequency on sites) was extremely high. It led to a month or two of burnout as the lead engineer on the team, despite probably leading to finding a hire sooner. The cost is, in my opinion, immeasurable.
  - bradleyjg 8y ago
    At least where I work, we are painfully aware of this tradeoff, but are willing to make the sacrifice because of the severe costs of making a bad hire. We are small, and cannot afford to lose 90 days. Small or large, I think this is fair. But also fair is to take this into account when making claims about any alleged shortage of qualified workers. Not that you or your company have necessarily made any such claims but plenty of companies that similarly follow a low false positive at the expense of false negatives approach have done so, including in sworn testimony before legislative bodies.
- inimino 8y ago
  I couldn't agree more. I think the illusion that hiring is a deterministic and rational process, combined with the fear of firing, leads to adding more and more filters, which just makes things worse. It's a really expensive and inefficient system, and tends to worsen as companies grow. It would be much better to hire fast and fire fast. As I recently blogged about this issue: You may be one of those companies that makes much of hiring only the top 1% of the top 1% of applicants. This can be good for morale (“We are the best of the best”) but a too-selective hiring process is quite hazardous. If you find a way to effectively measure your false-negative rate, (the no-hires that you should have hired) you may find out that half your current team wouldn’t make it through your current hiring process. If people commonly apply to your company three or four times before getting in, your highly-selective hiring process is probably filtering on random noise more than skill or talent. In other words, your precision and recall[1] are both bad, the bad results are coming from a costly process, and adding more filters just makes things worse. [1]: https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Precision_and_recall
crispyambulance 8y ago
I kind of resent these attempts at optimally cherry-picking "the right" candidates using data. Hiring is _intrinsically_ a subjective act. People are very much a moving target. They change over time. Work experiences, even bad ones, shape one's skills and ability to cope in organizations. Almost everybody has a bad-fit job at one time or another. The experience of a bad-fit is actually important to the growth of the individual and, I think, their coworkers and employers.
- AlexCoventry 8y ago
  When an organization scales to the point where they need something like TripleByte, they're going to be using some kind of impersonal screening process, though. This is better than some disinterested HR drone scanning resumes for keywords.
  - crispyambulance 8y ago
    I agree that an HR drone scanning for keywords is not good (though some are better than others). The most savvy candidates, however, tend to skip the step where you throw your resume into a giant vat with the others and hope for the best. Isn't it still the case that most jobs are filled using references and professional networks?
    - thomasmeeks 8y ago
      No, not at truly large companies -- Amazon could never fill its halls with just references and such. Even at smaller companies, however, there's some pushback to network hiring because it isn't particularly great for getting a diverse pool of talent. To be honest, though there are a lot of dangers with this sort of thing, I am a fan. End of the day, engineers should be great engineers, not great at hacking the job finding process.
      - mywittyname 8y ago
        > there's some pushback to network hiring because it isn't particularly great for getting a diverse pool of talent. I'd be surprised if random resumes are really that much more diverse than network hiring. Consider principles like the Erdos number[1] -- the collaborative distance between groups of people in similar fields is strikingly small. I bet, a company with a reasonably large development staff (50 people) is probably no more than 2-degrees separated from 99% of the talent pool in a given region. Just about every position I've looked at was with a company where a former colleague works. [1]https://en.wikipedia.org/wiki/Erd%C5%91s_number https://en.wikipedia.org/wiki/Erd%C5%91s_number
greatamerican 8y ago
I hope this company runs out of money. Writing an article like this shows how deeply they misunderstand the problem they are seeking to solve.
- paulie_a 8y ago
  Agreed, this approach is idiotic. I would avoid even applying for a company that took this approach.
yosito 8y ago
Not a fan of Triplebyte. I'm a full stack engineer with 10 years of experience building web apps, and while I'm not the best in the world, I'm still pretty damn good. I took Triplebyte's interview a few months ago, and they rejected me with a link to a tutorial on how to build your first webpage (https://learn.shayhowe.com/html-css/ https://learn.shayhowe.com/html-css/). This company knows absolutely nothing about hiring good engineers.
- greenhatman 8y ago
  That is hilariously insulting. Sick burn. I'd probably get similar. Also full stack 10 years.
acconrad 8y ago
On a side note, I took the 20-30 min front-end exam that is referenced in the article and a few things bug me (in case anyone from TripleByte is reading): 1. I did "exceptionally well" but I don't know how many I got right or in what percentile I fell in. Why? Firstly, I'm immediately suspect if I actually did that well. For all I know, the "top percent" could just be anyone in the top 50%. And the top 50% only gets 10 questions right. But more importantly, I already stated in the beginning I was taking it for fun, so why can't I learn what I got wrong so I can improve? I imagine there were areas I got wrong that were repeated, which brings me to my next point... 2. Why so React-centric? I happen to use it but plenty of people use Angular or Vue. You can't expect them to know about the React events lifecycle. 3. Okay, so I don't live in SF or NYC. But I know some of those bigger companies have offices in Boston, where I do live. Why can't you make that work if you already know what companies are in your pipeline and which offices they have? Seems super expensive and wasteful to lose out on a great engineer when you know your list of 200 companies totally has an office not in SF or NYC (e.g. Facebook). 4. Okay, so I want to work remote. Why can't I just take your final Google hangouts exam so that way you have on file "okay cool this person is great, we can fast track this person." If you green light companies who are remote-friendly, you don't have to worry about this issue. Plus isn't TripleByte a YC company, working with other YC companies? I know for a fact that GitLab is also YC and is a remote-friendly company. Not to mention you've advertised for remote engineers! https://news.ycombinator.com/item?id=15066073 https://news.ycombinator.com/item?id=15066073 I dunno, given that the article is all about touting how effective the 30 question exam is at screening out candidates, you'd think you'd want to do something useful with that quiz instead of locking people out, even if they don't fit your current criteria.
- KirinDave 8y ago
  Why would you give them all that data for free?
- compumike 8y ago
  (Original article author here.) Feedback is important, so after the interview, everyone gets detailed personal feedback on every section, regardless of the outcome. We get much higher resolution data after the interview (part of the point of my article). You may have hit a few React-specific questions, but getting those right is certainly not required. As far as Boston/remote: interviews are expensive for us, so it makes sense to interview engineers who are excited about working in places where lots and lots of our companies are hiring today. Currently that's SF or NYC. When we expand, you'll be able to pick up from that step.
KirinDave 8y ago
Am I the only person getting progressively more creeped out by the series of bizarre, unsourced, pseudo-scientific ads TripleByte has been running? On Reddit right now they're running this weird faux-linear-algebra thing where they imply that they can build a vector of your skills vs. a vector of job requirements and get a meaningful answer via the dot product. Which is a bit like saying you can predict the weather by taking the dot product of a vector of ocean and air. What even are the units? What does any of this even mean? Hiring is a challenging, multi-dimensional thing. It involves a high-risk and ideally informed decision by multiple parties. Doing it effectively is hard. Doing it effectively and respectfully is harder still. And yet TripleByte comes in and says, "We sound vaguely like machine learning. We got this." Honestly, they make HackerRank, which was another extremely sketchy organization making a lot of very questionable decisions, look reasonable by comparison.
- kofejnik 8y ago
  Vector thing actually does make some sense, you might want to read about embedding. Or just watch fast.ai lesson 5. Ironically, this was the topic of my final (and failed) conversation with them
  - sidlls 8y ago
    How does embedding apply here? What's the justification?
    - kofejnik 8y ago
      It works very nicely for predicting what movie you'd like to watch, so it could potentially work with jobs, too. Collecting enough data could be challenging, though
      - sidlls 8y ago
        That's an incredibe over-simplification of the utility and usage of embedding. For an embedding to work there must be some legitimate (as opposed to arbitrary or even non-existent) relationship to be teased out.
kofejnik 8y ago
Just my $0.02 about triplebyte - I went through all interviews, seemingly doing fine, but after the last informal talk (which also seemed ok to me), there was about 10 days of total silence. Finally, I emailed to enquire and immediately received an 'Unfortunately, ...' letter. So, in the end, I've had 4 hangouts sessions over 3 weeks, plus time spent preparing, and was rejected with no feedback at all. I'm still curious, was it the bloom filter?
- kofejnik 8y ago
  update: it has been fixed very quickly, and I'm a fan of triplebyte as a result
asadlionpk 8y ago
I will be harsh. This looks more like "hey, watch us force some math onto this topic and look cool". Sadly, this might impress some dumb CEO to use them though.
mlthoughts2018 8y ago
My experience hiring machine learning talent over several years has been that people over-hype the cost of a false positive. Both the article's false positive (expending the cost of interviewing on someone who ultimately reveals to be the wrong fit) and also a more fundamental false positive: actually hiring someone who would hypothetically fail a lot of these interview pipelines. The discussions about making these pipelines more quantitative, with assessments and quizzes, always couches it with a tacit assumption that the worst outcome would be to actually hire someone who fails at one of these interviews. Rejecting a good person sucks, as they say, but not as much as hiring one of the multitude of sneaky, low-skilled fakers out there. And of course, everybody's got their hot new take on how to spot the supposedly huge population of fakers. What I have learned is two-fold: (1) That person who aced all your interviews and finally looked like the perfect person to hire probably just spent 3-6 months utterly failing at a bunch of other interviews, just to get into "interview shape," refresh on all the nonsense hazing-style whiteboard trivia about data structures that they had never needed in years at their job, etc. So it's totally asinine to believe that someone passing through all your filters must be the sort of person who would rarely fail some filters. That person almost surely did fail filters, and the companies where they failed believe they dodged a costly false-positive bullet, while you believe you just made an offer to the greatest engineer. Hopefully you can see the myopia here. (2) The cost of passing up a good-but-failed-at-interview-trivia engineer is often far greater than the cost of hiring them. For one thing, "suboptimal-at-interviews" engineers are pretty damn good engineers, and they can do things that differ from esoteric algorithm trivia, such as helping your business make money. Another thing is that many engineers can generalize what they learn, generalize from example code or templates, etc., very efficiently. So while they might reveal a weakness by failing part of an interview (and everybody has such weaknesses), why do you really care? They can probably become an expert on that weakness topic in a matter of months if they work on it every day, or if you have existing employees who can mentor them. But the biggest thing is part of what Paul Graham wrote in "Great Hackers": good engineers tend to cluster and want to work with other good engineers. So if you're sitting there without already having a few good engineers on your team, then most likely, the cost to mistakenly rejecting a great candidate who happened to have a bad day, or a great candidate who happens to hate writing tree algorithms on whiteboards, leaves you running a huge risk of losing out on a good engineer who could help kickstart the phenomenon of getting the next good engineer. When your team is in this stage, you absolutely can manage with a few "dud" hires who need a lot of help or who have skill gaps in key areas. The cost of adding them to the team and managing their "suboptimality" is far less than the continued search costs brought on by rejecting good candidates with and overly risk averse hiring threshold, and leaving your team in the state of affairs where it still doesn't have a good engineer to help attract more. In other words, the loss function penalizes false negatives more severely than the combined penalty from effort spent on true negatives and suboptimality / management costs of false positives. But all these skeezy interview-as-aservice businesses what you to believe that the opposite is true, that if you accidentally hire a "faker" because your hiring process was too easy, then Cthulhu is going to rise out of the sea and lay waste to your company. Of course they want you to believe that. That's how they make money. Preying on your fears over what would happen if you just unclench and treat candidates like human beings with strengths and flaws and don't hold them up to ludicrous standards that lead to self-selecting macho 22-year-olds getting hired because they just spent 10 months on leetcode. When you start to realize this, it becomes obvious that onerous code tests, brainless data structure esoterica, hazing-style coding interviews, and especially businesses that offer to outsource that nonsense, like TripleByte, is all just snake oil junk.
burnte 8y ago
The problem isn't the computers, it's the people. You put HR-bots on the task of listing the job, and they don't know Atom from Adam, so they list all sorts of silly requirements like 15 years of SAP experience, 15 years of Ruby on Rails, 15 years of COBOL, and all for a $20/hr entry position, or a list of certifications that no human could ever accumulate. Then what happens is applicants start keyword spamming their resumes just to get noticed, and now as a technical person I get a stack of resumes that are absolute trash. Two years ago I was hiring for a sysadmin. My HR department put my requirements up on Indeed. I got 70 resumes that passed their screening. Of those 70 I found 5 that I wanted to interview, 3 that showed up, and none were hirable. I left a company several months ago, couldn't deal with the management anymore, and the past few months of job searching have been excruciating. We need more technical people screening resumes and comparing to actual job requirements.

50 points by Harj 8y ago

deleted 8y ago

[deleted]

Edit: I think I've run afoul of an anti-triplebyte sentiment. I should clarify that I think this post did a good job building a very simple example of the statistical theory behind assessment,but I have no idea whether their product / approach is reasonable or not. Building a good assessment is much more than just statistics, and it sounds from other comments that there are serious concerns about the validity of their tool. Really enjoyed the build up from simple cases, to more complex models! If you're interested in the statistics behind estimating skill, and how well questions tell apart novices from experts, check out item response theory :) https://en.m.wikipedia.org/wiki/Item_response_theory https://en.m.wikipedia.org/wiki/Item_response_theory

compumike 8y ago

Companies seem to be adding more screening steps to try to reduce their false positive rate -- the rate at which they interview people who aren't hireable. But most don't seem to understand that mathematically, there's a tradeoff for a higher false negative rate. Screening false negatives are people who would have done well in an interview, but don't make it to that stage. These are more hidden to the company, but quite expensive for the hiring process, and painful for people who are wrongly rejected. If we put this in probabilistic terms, I hope we can have a deeper conversation about what's happening and how this issue impacts engineers.

tmpz22 8y ago
What I'm worried about is the b2b crowdsourcing of these rankings between companies. Corporations have shown an affinity for sharing data, like Facebook and Google buying ad targeting data. I feel like it is only a matter of time before your tinder data, resume data, etc., is all fed into your credit score, etc., to the point where a minority of the population is completely screwed over by false negative rates made by bad data scientists.
- sjg007 8y ago
  Any type of score is regulated by the Fair Credit and reporting act. These scores may fall under that regulation, especially if they are operating as a clearinghouse.
  - CoolGuySteve 8y ago
    That's true for your credit score, but these screenings could effectively blacklist someone from the industry if a bad encounter with TripleByte or someone like them is shared between all employers.
    - sjg007 8y ago
      Still relevant and still covered.
- itronitron 8y ago
  I wouldn't be surprised if TripleByte has already triangulated data with HN or other YC companies to get additional profile information on applicants. The unpredictability of what will show up on the tests, and the unknown future of my 'grade', is a major turn-off. I'd prefer to see a certification process, where you can study for the test, then take the test, and pay to retake it if you want to improve your score. If the technical knowledge in the tests is that important for their clients, then sharing that openly will increase the pool of successful candidates over the long term.
n42 8y ago
At least where I work, we are painfully aware of this tradeoff, but are willing to make the sacrifice because of the severe costs of making a bad hire. We are small, and cannot afford to lose 90 days. I realize this article refutes this point of view, but finance and HR do not see it this way. I am more happy to devote several hours of each week interviewing than entire days/weeks mentoring and reviewing code for a bad hire, or worse, dealing with the widespread consequences of a culture misfit in a small team. Hiring is hard.
- compumike 8y ago
  (Original article author here.) I'd distinguish between screening false positives (which my article focuses on) and hiring false positives. Screening rejections (before the final interview) are usually done with very limited information, so there's more room for bias and noise. Hiring is hard! :)
  - n42 8y ago
    You're right, the difference is important. I've been the tail end of the interview funnel when experimenting with our screening strictness. The emotional toll that less stringent screening had on my day-to-day work (faster, rapid, and high frequency on sites) was extremely high. It led to a month or two of burnout as the lead engineer on the team, despite probably leading to finding a hire sooner. The cost is, in my opinion, immeasurable.
- bradleyjg 8y ago
  At least where I work, we are painfully aware of this tradeoff, but are willing to make the sacrifice because of the severe costs of making a bad hire. We are small, and cannot afford to lose 90 days. Small or large, I think this is fair. But also fair is to take this into account when making claims about any alleged shortage of qualified workers. Not that you or your company have necessarily made any such claims but plenty of companies that similarly follow a low false positive at the expense of false negatives approach have done so, including in sworn testimony before legislative bodies.
inimino 8y ago
I couldn't agree more. I think the illusion that hiring is a deterministic and rational process, combined with the fear of firing, leads to adding more and more filters, which just makes things worse. It's a really expensive and inefficient system, and tends to worsen as companies grow. It would be much better to hire fast and fire fast. As I recently blogged about this issue: You may be one of those companies that makes much of hiring only the top 1% of the top 1% of applicants. This can be good for morale (“We are the best of the best”) but a too-selective hiring process is quite hazardous. If you find a way to effectively measure your false-negative rate, (the no-hires that you should have hired) you may find out that half your current team wouldn’t make it through your current hiring process. If people commonly apply to your company three or four times before getting in, your highly-selective hiring process is probably filtering on random noise more than skill or talent. In other words, your precision and recall[1] are both bad, the bad results are coming from a costly process, and adding more filters just makes things worse. [1]: https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Precision_and_recall

crispyambulance 8y ago

I kind of resent these attempts at optimally cherry-picking "the right" candidates using data. Hiring is _intrinsically_ a subjective act. People are very much a moving target. They change over time. Work experiences, even bad ones, shape one's skills and ability to cope in organizations. Almost everybody has a bad-fit job at one time or another. The experience of a bad-fit is actually important to the growth of the individual and, I think, their coworkers and employers.

AlexCoventry 8y ago
When an organization scales to the point where they need something like TripleByte, they're going to be using some kind of impersonal screening process, though. This is better than some disinterested HR drone scanning resumes for keywords.
- crispyambulance 8y ago
  I agree that an HR drone scanning for keywords is not good (though some are better than others). The most savvy candidates, however, tend to skip the step where you throw your resume into a giant vat with the others and hope for the best. Isn't it still the case that most jobs are filled using references and professional networks?
  - thomasmeeks 8y ago
    No, not at truly large companies -- Amazon could never fill its halls with just references and such. Even at smaller companies, however, there's some pushback to network hiring because it isn't particularly great for getting a diverse pool of talent. To be honest, though there are a lot of dangers with this sort of thing, I am a fan. End of the day, engineers should be great engineers, not great at hacking the job finding process.
    - mywittyname 8y ago
      > there's some pushback to network hiring because it isn't particularly great for getting a diverse pool of talent. I'd be surprised if random resumes are really that much more diverse than network hiring. Consider principles like the Erdos number[1] -- the collaborative distance between groups of people in similar fields is strikingly small. I bet, a company with a reasonably large development staff (50 people) is probably no more than 2-degrees separated from 99% of the talent pool in a given region. Just about every position I've looked at was with a company where a former colleague works. [1]https://en.wikipedia.org/wiki/Erd%C5%91s_number https://en.wikipedia.org/wiki/Erd%C5%91s_number

greatamerican 8y ago

I hope this company runs out of money. Writing an article like this shows how deeply they misunderstand the problem they are seeking to solve.

paulie_a 8y ago
Agreed, this approach is idiotic. I would avoid even applying for a company that took this approach.

yosito 8y ago

Not a fan of Triplebyte. I'm a full stack engineer with 10 years of experience building web apps, and while I'm not the best in the world, I'm still pretty damn good. I took Triplebyte's interview a few months ago, and they rejected me with a link to a tutorial on how to build your first webpage (https://learn.shayhowe.com/html-css/ https://learn.shayhowe.com/html-css/). This company knows absolutely nothing about hiring good engineers.

greenhatman 8y ago
That is hilariously insulting. Sick burn. I'd probably get similar. Also full stack 10 years.

acconrad 8y ago

On a side note, I took the 20-30 min front-end exam that is referenced in the article and a few things bug me (in case anyone from TripleByte is reading): 1. I did "exceptionally well" but I don't know how many I got right or in what percentile I fell in. Why? Firstly, I'm immediately suspect if I actually did that well. For all I know, the "top percent" could just be anyone in the top 50%. And the top 50% only gets 10 questions right. But more importantly, I already stated in the beginning I was taking it for fun, so why can't I learn what I got wrong so I can improve? I imagine there were areas I got wrong that were repeated, which brings me to my next point... 2. Why so React-centric? I happen to use it but plenty of people use Angular or Vue. You can't expect them to know about the React events lifecycle. 3. Okay, so I don't live in SF or NYC. But I know some of those bigger companies have offices in Boston, where I do live. Why can't you make that work if you already know what companies are in your pipeline and which offices they have? Seems super expensive and wasteful to lose out on a great engineer when you know your list of 200 companies totally has an office not in SF or NYC (e.g. Facebook). 4. Okay, so I want to work remote. Why can't I just take your final Google hangouts exam so that way you have on file "okay cool this person is great, we can fast track this person." If you green light companies who are remote-friendly, you don't have to worry about this issue. Plus isn't TripleByte a YC company, working with other YC companies? I know for a fact that GitLab is also YC and is a remote-friendly company. Not to mention you've advertised for remote engineers! https://news.ycombinator.com/item?id=15066073 https://news.ycombinator.com/item?id=15066073 I dunno, given that the article is all about touting how effective the 30 question exam is at screening out candidates, you'd think you'd want to do something useful with that quiz instead of locking people out, even if they don't fit your current criteria.

KirinDave 8y ago
Why would you give them all that data for free?
compumike 8y ago
(Original article author here.) Feedback is important, so after the interview, everyone gets detailed personal feedback on every section, regardless of the outcome. We get much higher resolution data after the interview (part of the point of my article). You may have hit a few React-specific questions, but getting those right is certainly not required. As far as Boston/remote: interviews are expensive for us, so it makes sense to interview engineers who are excited about working in places where lots and lots of our companies are hiring today. Currently that's SF or NYC. When we expand, you'll be able to pick up from that step.

KirinDave 8y ago

Am I the only person getting progressively more creeped out by the series of bizarre, unsourced, pseudo-scientific ads TripleByte has been running? On Reddit right now they're running this weird faux-linear-algebra thing where they imply that they can build a vector of your skills vs. a vector of job requirements and get a meaningful answer via the dot product. Which is a bit like saying you can predict the weather by taking the dot product of a vector of ocean and air. What even are the units? What does any of this even mean? Hiring is a challenging, multi-dimensional thing. It involves a high-risk and ideally informed decision by multiple parties. Doing it effectively is hard. Doing it effectively and respectfully is harder still. And yet TripleByte comes in and says, "We sound vaguely like machine learning. We got this." Honestly, they make HackerRank, which was another extremely sketchy organization making a lot of very questionable decisions, look reasonable by comparison.

kofejnik 8y ago
Vector thing actually does make some sense, you might want to read about embedding. Or just watch fast.ai lesson 5. Ironically, this was the topic of my final (and failed) conversation with them
- sidlls 8y ago
  How does embedding apply here? What's the justification?
  - kofejnik 8y ago
    It works very nicely for predicting what movie you'd like to watch, so it could potentially work with jobs, too. Collecting enough data could be challenging, though
    - sidlls 8y ago
      That's an incredibe over-simplification of the utility and usage of embedding. For an embedding to work there must be some legitimate (as opposed to arbitrary or even non-existent) relationship to be teased out.

kofejnik 8y ago

Just my $0.02 about triplebyte - I went through all interviews, seemingly doing fine, but after the last informal talk (which also seemed ok to me), there was about 10 days of total silence. Finally, I emailed to enquire and immediately received an 'Unfortunately, ...' letter. So, in the end, I've had 4 hangouts sessions over 3 weeks, plus time spent preparing, and was rejected with no feedback at all. I'm still curious, was it the bloom filter?

kofejnik 8y ago
update: it has been fixed very quickly, and I'm a fan of triplebyte as a result

asadlionpk 8y ago

I will be harsh. This looks more like "hey, watch us force some math onto this topic and look cool". Sadly, this might impress some dumb CEO to use them though.

mlthoughts2018 8y ago

My experience hiring machine learning talent over several years has been that people over-hype the cost of a false positive. Both the article's false positive (expending the cost of interviewing on someone who ultimately reveals to be the wrong fit) and also a more fundamental false positive: actually hiring someone who would hypothetically fail a lot of these interview pipelines. The discussions about making these pipelines more quantitative, with assessments and quizzes, always couches it with a tacit assumption that the worst outcome would be to actually hire someone who fails at one of these interviews. Rejecting a good person sucks, as they say, but not as much as hiring one of the multitude of sneaky, low-skilled fakers out there. And of course, everybody's got their hot new take on how to spot the supposedly huge population of fakers. What I have learned is two-fold: (1) That person who aced all your interviews and finally looked like the perfect person to hire probably just spent 3-6 months utterly failing at a bunch of other interviews, just to get into "interview shape," refresh on all the nonsense hazing-style whiteboard trivia about data structures that they had never needed in years at their job, etc. So it's totally asinine to believe that someone passing through all your filters must be the sort of person who would rarely fail some filters. That person almost surely did fail filters, and the companies where they failed believe they dodged a costly false-positive bullet, while you believe you just made an offer to the greatest engineer. Hopefully you can see the myopia here. (2) The cost of passing up a good-but-failed-at-interview-trivia engineer is often far greater than the cost of hiring them. For one thing, "suboptimal-at-interviews" engineers are pretty damn good engineers, and they can do things that differ from esoteric algorithm trivia, such as helping your business make money. Another thing is that many engineers can generalize what they learn, generalize from example code or templates, etc., very efficiently. So while they might reveal a weakness by failing part of an interview (and everybody has such weaknesses), why do you really care? They can probably become an expert on that weakness topic in a matter of months if they work on it every day, or if you have existing employees who can mentor them. But the biggest thing is part of what Paul Graham wrote in "Great Hackers": good engineers tend to cluster and want to work with other good engineers. So if you're sitting there without already having a few good engineers on your team, then most likely, the cost to mistakenly rejecting a great candidate who happened to have a bad day, or a great candidate who happens to hate writing tree algorithms on whiteboards, leaves you running a huge risk of losing out on a good engineer who could help kickstart the phenomenon of getting the next good engineer. When your team is in this stage, you absolutely can manage with a few "dud" hires who need a lot of help or who have skill gaps in key areas. The cost of adding them to the team and managing their "suboptimality" is far less than the continued search costs brought on by rejecting good candidates with and overly risk averse hiring threshold, and leaving your team in the state of affairs where it still doesn't have a good engineer to help attract more. In other words, the loss function penalizes false negatives more severely than the combined penalty from effort spent on true negatives and suboptimality / management costs of false positives. But all these skeezy interview-as-aservice businesses what you to believe that the opposite is true, that if you accidentally hire a "faker" because your hiring process was too easy, then Cthulhu is going to rise out of the sea and lay waste to your company. Of course they want you to believe that. That's how they make money. Preying on your fears over what would happen if you just unclench and treat candidates like human beings with strengths and flaws and don't hold them up to ludicrous standards that lead to self-selecting macho 22-year-olds getting hired because they just spent 10 months on leetcode. When you start to realize this, it becomes obvious that onerous code tests, brainless data structure esoterica, hazing-style coding interviews, and especially businesses that offer to outsource that nonsense, like TripleByte, is all just snake oil junk.

burnte 8y ago

The problem isn't the computers, it's the people. You put HR-bots on the task of listing the job, and they don't know Atom from Adam, so they list all sorts of silly requirements like 15 years of SAP experience, 15 years of Ruby on Rails, 15 years of COBOL, and all for a $20/hr entry position, or a list of certifications that no human could ever accumulate. Then what happens is applicants start keyword spamming their resumes just to get noticed, and now as a technical person I get a stack of resumes that are absolute trash. Two years ago I was hiring for a sysadmin. My HR department put my requirements up on Indeed. I got 70 resumes that passed their screening. Of those 70 I found 5 that I wanted to interview, 3 that showed up, and none were hirable. I left a company several months ago, couldn't deal with the management anymore, and the past few months of job searching have been excruciating. We need more technical people screening resumes and comparing to actual job requirements.