pradeep_m

I think this is a rather harsh take. Most of the people working on automating it understand how hard it is and are striving through it. You need some of these irrational believers for progress!

It always seems like incumbents have it all (resources, distribution, talent, etc) until they don't. For ex, the "iPhone" theoretically should have come from Nokia by that logic. AR particularly requires truly novel innovations for it to work in mass-market. There might be niche-cases that work for AR (studio/arcade games, sports tech) before the mass market use-cases.

Fair question. We collect data about every interviewer (at a very granular level to understand what skills they are good at evaluating and how predictive they are).

Also, we provide interviewing history data as part of the evaluation. For example, if you knew the last 10 candidates a particular interviewer has endorsed have ended up at companies like Airbnb, Stripe, Google, Facebook, etc, would the interviewer's reputation still be in question?

Oh - thanks for the feedback! We'll fix.

Yes, I'd agree they are a closer comparable. Our primary difference is that our interview process is personalized to each individual candidate because we match candidates to an independent interviewer relevant to their domain and field of interest. We don't specify what the interviewer should ask. Instead our direction to them is: "We matched this candidate to you because they expressed these skills/interests. Try to identify where their strengths are and if you would recommend them for a role in your specialty."

The evaluations our interviewers write as a result gives us a lot of nuance and evidence to match candidates with the best roles for them.

The fact that the interviewers are independent is also another differentiator. This gives candidates an opportunity to make a valuable connection, and we expect interviewers to take ~10-15 min at the end to share feedback on the candidate's performance, how they can improve, and answer any questions / share personalized advice. We have heard many frustrated accounts from our candidates about receiving rejections with no feedback and how this is one of their most valued benefits of our approach.

On the scaling point - it's a very good question. We are collecting a lot of data about each interviewer and specifically their skill at evaluating skill (i.e., how predictive is their evaluation). So over time, we will be able to understand interviewer strength and what they are qualified to evaluate, and seniority of the interviewer becomes less important.

ha, I agree we are a recruiting company. However, we do believe we are solving a couple of different pain points.

a) For companies, resume doesn't convey the complete picture and any additional signal would be super valuable. We believe the level of signal you get from a senior interviewer is far more valuable than resume-based signals or coding test-based signals.

b) For candidates, they get feedback directly from the senior engineers about what they are good at and what they should improve on. Most companies provide no feedback at all and that can be frustrating! In addition to that, if you do well, you get a direct referral from the senior engineer to all startups. And because of the referral, you get fast-tracked to onsites!

Yes, we work with junior engineers too.

Hired, Vettery and other recruiting websites typically connect candidates to companies based on almost little to no vetting (Their vetting is based on your resume: essentially pedigree).

On HiDimensional, as a candidate, you interview with a senior engineer in your field and based on what you are strongest at, you get a personal referral from that engineer to the companies we work with. The engineer fills out an evaluation highlighting your strengths and we match you with the companies looking for your strengths.

Also, you get very clear feedback about what you are good at and what you can improve on from the senior engineers directly. For example, if you are an ML engineer, do you believe it'd be useful to interview with and get a direct referral from the former Head of ML at Quora to all startups?

The end goal is the same, but this approach yields a better experience for all parties.

We are the co-founders of HiDimensional. Very simply, we pair engineering candidates with senior engineers in their field and the senior engineers refer the top candidates to the 40+ top startups on our platform. It's common knowledge that referrals are important in hiring, and we believe this is a more efficient and meritocratic approach to increasing access and impact of referrals.

Would love your feedback and check out our blog post for more info.

Thanks!

I think the code is written for the case where it fits in memory. For that case, the solution is O(n) in terms of time complexity. From the feedback it seems that they only spoke about the solution where the data doesn't fit in memory.

Couldn't we train it on top of resumes itself? With every resume, you know where they worked before and you have the data of where they worked until then. Granted, it would be muddled with noisy data (like skills, etc that they didn't have in the past). It wouldn't be very hard to collect enough resumes as training data.

While I agree that interviews have high variance and a lot of times the interviews don't test what's required for the job, mini projects are a huge time sink for candidates too. Most candidates aren't going to spend a couple of days working on a project just to apply to a company. Unless all companies decide to just switch to using project-based interviewing, interviewing is here to stay (despite its quirks).

True, but how often are candidates truly rejected because of non-technical abilities? I'd guesstimate that 75% of candidates are rejected because of technical abilities (didn't get design right or algorithm right or coding standard is poor, etc).

I understand that there is variance in interviews. However, I don't understand how interviewing.io is fixing it? Or are you just collecting data to prove it?

Well, more than 50% of the companies have negative returns in the public market. So why do people invest in the public markets? For LPs, investing in VC firms is just to way to diversify and invest in private markets.