<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>hcknwscommenter</author><text>Left unsaid is the &amp;quot;everyone gets a veto&amp;quot; crap that destroys hiring 10x contributors. I have personally been involved in numerous interviews (as the interviewer ), where my fellow interviewers have deliberately shitcanned candidates because they seemed extremely well qualified and highly motivated (as compared to my colleague).  Everyone gets a veto is almost uniform in my field and it is a massive problem.</text><parent_chain><item><author>starpilot</author><text>Good source of data for training a de-bullshitter.&lt;p&gt;Input: A year and a half ago when we began scouting for this type of machine learning-savvy engineer —something we now call the machine learning Software Engineer (ML SWE) — it wasn’t something we knew much about. We looked at other companies’ equivalent roles but they weren’t exactly contextualized to Lyft’s business setting. This need motivated an entirely new role that we set up and started hiring for.&lt;p&gt;Target: We invented a position called a machine learning software engineer.&lt;p&gt;Input: First, candidates on the ML SWE loop go through Lyft’s hiring review. The review is a regularly scheduled session for a committee to study candidates with an unbiased perspective and decide whether to hire them. Working alongside the review committee is a separate panel of interviewers that provides technical feedback. This feedback is designed to help the committee decide if there’s a fit and, if so, the candidate’s technical level. At first glance, this review process may seem cumbersome. Examining the checks and balances more carefully, however, we notice that they are intentionally introduced to put friction on the hiring process. Having a consistent review committee unifies standards and eliminates bias.&lt;p&gt;Target: A committee and a group of interviewers evaluate a candidate for fit and technical level.&lt;p&gt;Input: Despite the what-ifs, being transparent about how we design interviews can improve our interviews. Call it enlightened self-interest: candidates invest time to talk to us and we mutually benefit from learning if there is a good fit. Even if there isn’t an immediate fit, positive experiences build brand and improves candidate sourcing. Maybe the candidate can reapply when the timing is better. Practically, hiring an engineer easily costs tens of thousands of dollars. By showing how we iterate on our interviews, we reveal what we truly care about and how we try to probe at them, hopefully adding to the virtuous cycle for the hiring pipeline.&lt;p&gt;Target: We don&amp;#x27;t respect the time of our readers and are hopelessly unenlightened on this fact.</text></item></parent_chain></comment><story><title>The Machine Learning Software Engineering Interview</title><url>https://eng.lyft.com/how-lyft-designs-the-machine-learning-software-engineering-interview-bbbb9fc8bb28</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>inerte</author><text>I have another target for your second one: We share a Google Drive full of resumes and people put their names on a spreadsheet when they like the candidate. Then we interview them!&lt;p&gt;It’s super innovative and fit to our business context!</text><parent_chain><item><author>starpilot</author><text>Good source of data for training a de-bullshitter.&lt;p&gt;Input: A year and a half ago when we began scouting for this type of machine learning-savvy engineer —something we now call the machine learning Software Engineer (ML SWE) — it wasn’t something we knew much about. We looked at other companies’ equivalent roles but they weren’t exactly contextualized to Lyft’s business setting. This need motivated an entirely new role that we set up and started hiring for.&lt;p&gt;Target: We invented a position called a machine learning software engineer.&lt;p&gt;Input: First, candidates on the ML SWE loop go through Lyft’s hiring review. The review is a regularly scheduled session for a committee to study candidates with an unbiased perspective and decide whether to hire them. Working alongside the review committee is a separate panel of interviewers that provides technical feedback. This feedback is designed to help the committee decide if there’s a fit and, if so, the candidate’s technical level. At first glance, this review process may seem cumbersome. Examining the checks and balances more carefully, however, we notice that they are intentionally introduced to put friction on the hiring process. Having a consistent review committee unifies standards and eliminates bias.&lt;p&gt;Target: A committee and a group of interviewers evaluate a candidate for fit and technical level.&lt;p&gt;Input: Despite the what-ifs, being transparent about how we design interviews can improve our interviews. Call it enlightened self-interest: candidates invest time to talk to us and we mutually benefit from learning if there is a good fit. Even if there isn’t an immediate fit, positive experiences build brand and improves candidate sourcing. Maybe the candidate can reapply when the timing is better. Practically, hiring an engineer easily costs tens of thousands of dollars. By showing how we iterate on our interviews, we reveal what we truly care about and how we try to probe at them, hopefully adding to the virtuous cycle for the hiring pipeline.&lt;p&gt;Target: We don&amp;#x27;t respect the time of our readers and are hopelessly unenlightened on this fact.</text></item></parent_chain></comment><story><title>The Machine Learning Software Engineering Interview</title><url>https://eng.lyft.com/how-lyft-designs-the-machine-learning-software-engineering-interview-bbbb9fc8bb28</url></story>