Anonymous interview evals of strong software/ML engineering candidates

Hacker News

We are a recruiting startup with a small twist. We represent engineering candidates who receive a recommendation through our personalized vetting process, which includes a technical interview with an unbiased third-party senior engineer. We match the candidates with interviewers based on their background and the roles they are looking for. We pay for the senior engineer's time to interview so that the feedback is completely unbiased. These interviews are unique in that they are not set up to test if a candidate is a fit for a pre-set role, but instead they are personalized to extract each candidate's strengths and the roles in which they'd excel. The senior engineers who interview for us have collectively interviewed 1000s of candidates and have built and led engineering teams at top tier startups and bigger companies (e.g., Google, Facebook, Uber, etc.). Here are the two evals Candidate1: https://goo.gl/U4jPR7 Candidate2: https://goo.gl/VXMXRF We'd love feedback on the two candidates and our interviewers' evaluations of them. - Does the feedback give you a good sense of the candidate's strengths and the environments they'll do well in? - Would this save time in your evaluation process because the candidate has already been recommended after a technical interview? - Do you want to interview this candidate for your own team? Why or why not? If interested in these candidates or other vetted candidates with full evaluations and interviewer identity, please feel free to reach out to ngptprad@gmail.com

Visit product View on Hacker NewsJul 31, 2017

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Product HuntOn track for Day 1 leaderboard · Strong signals: google · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, 000 · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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