Hacker News Reader

Comment by jerrythegerbil | Hacker News Reader

> I fail 65% of the time. Same exact resume, different luck.

As someone who’s run hiring pipelines for technical roles in the past few years, that’s actually a fantastic number. I objectively hate saying that, but it’s true.

35% chance of elevating a technical individual to the next stage with no effort? I’ve seen as many as 100+ applicants an hour even when including a domain specific screener question. That’s 35 “screened” applicants in an hour. Were valid candidates screened out? Yes. Does you still have a candidate pool 35x larger than you need? Unfortunately, also yes.

The volume of applicants is SO HIGH such that your chances of getting moved to the next stage are actually markedly worse if AI isn’t involved. If you didn’t apply immediately (using an AI bot) there’s 50+ people ahead of you, and an exhausted technical leader if they ever make it to your resume.

Referral bonuses exist for a reason.

View on HN

In that case, I have a pre-screening system to sell you. Through state of the art technology, it only lets through the best* 1% of applications.

*According to our proprietary, undisclosed, non-deterministic metric, which may or may not be Math.random

Reminds me of this

https://stackoverflow.com/questions/16833100/why-does-the-mo...

I worked at a startup that judged their hiring pipeline quality using rejection rate criteria.

Is it? Or is it a 65% chance of a resume getting ignored before a single human sees it, reducing your pipeline's likelihood of catching qualified candidates by the same?

Gates that reduce resume flow-through are only useful if their reduction is correlated with quality. Otherwise they're just dragging out your hiring process or unnecessarily causing you to ultimately lower your hiring bars.

The goal for the interviewer is to have a much higher ratio of good/bad candidates after the first screening. This means the more costly time you spend on the second step has a better return.

> Gates that reduce resume flow-through are only useful if their reduction is correlated with quality.

The volume is infeasible to review everyone for quality, even at an hour scale. The conclusion and solution is inevitable, though I wish it were different. 35% is actually really good if you’re not coming in through a referral.

The current reality is <1% and the person reviewing you is exhausted.

This reasoning isn't.

What a inhumane way of looking at this. Hiring is deeply flawed, you know it, and yet you keep job postings open for weeks/months in case "the one" magically appears on your doorstep instead of just interviewing 10-20 people and just pick one...

Corpo bullshittery at its finest.

What's the alternative? Everyones up in arms, but I see ZERO viable alternatives proposed.

If you have 1000 applications for every job, and you know that a bunch of these applications are "a bad fit", to put it mildly, you have to filter. And you cannot realistically give every resume a good, human look. By the time HR would be done, the market has already moved on five times.

So, what is the real difference between being overlooked because HR could only look at the first 100 resumes, or the AI filtered all 1000 resumes down to 100? In the end, a fuckton of potentially great people get their feelings hurt either way.

>instead of just interviewing 10-20 people and just pick one

Here's a realistic proposition. HR just wants to inflate numbers so that they seem busy looking for the right fit. Keep posting open for 1 week, manually filter for another week, invite people, employ one. Plenty of people with degrees looking for jobs right now, I don't see what's the issue with just trying one. Companies desperately look for the "magic" applicant that checks all boxes, while also trying to pay them almost minimum wage.

If your hiring pipeline is employing a filter that a) is not better than a random chance and b) is expensive to implement get rid of the filter.

Instead of spending all those resources on resume filtering, hire resume blind. Instead of using llms for a thing they are bad at (subjective decision making) use them to build a deterministic process that isn’t.

Use work sample hiring as the filter. Make the work sample automatic to sign up for and judge.

great question. The alternative is not accepting 1000 applicants. Nobody said you have to keep up your job posting for two weeks, or two hours for that matter. stop once you have enough. Enough is defined by whatever number you would have filtered to. In the rare case none of the first ten applicants were appropriate, just open it again until youve got another tranche.

That's just another type of randomness (who was online during the short time the posting was opened).

At least this would not force applicants to fine tune their applications to the latest LLM bullshit bingo.

right. But if you go online and look for a job, then the ones you are available at that moment will actually read your application

"Being online during the short time" heavily favors bots. In a way, AI screening tools saved us from the future of everybody buying resume-spamming-as-a-service because it became as important to use these as getting a college degree.

You are assuming quality applicants are evenly distributed in terms of time of application - they aren’t. If you cut off at 100, you will only get a sample of people spewing fully automated application bots which mostly aren’t what you want.

If that's true, then it suggests an easy fix: leave your application up for four hours, then discard all applications you get for the first two.

> If you have 1000 applications for every job, and you know that a bunch of these applications are "a bad fit", to put it mildly, you have to filter. And you cannot realistically give every resume a good, human look.

At 10 seconds per resume, it would take you 3 hours to go through all 1000 resumes. I don't know what you consider "good" and "human", but my human eyes could easily do good enough, fully manual pre-screening at a rate of 1 requisition per day.

> At 10 seconds per resume, it would take you 3 hours to go through all 1000 resumes.

At 10 seconds per resume, I would not assume that you're screening better than the LLM.

You could remove the least relevant resumes very quickly. Maybe not 10 seconds, but 30 seconds per resume for sure.

What I hear happens now: people apply for a "Senior Golang SWE" with 2 years of experience with C#. Or, relevant for hiring in the US, the job posting says the visa is required, but people apply without it anyway.

It’s weird because unemployment is still quite low, right?

Maybe a platform could be designed where candidates have one account for multiple companies, and the number of applications on the platform is limited to, say, ten per person per month or something. To get people to be selective. I don’t think this should be the only way to apply, but maybe the companies involved could look there first.

You may as well just randomly pick 65 to discard, if your only goal is to reduce the number for review.

That’s exactly it for large scale hiring with finite resources.

It’s all probabilities in the end. And if an LLM gives you more a more relevant pool vs random distribution, that’s still a net benefit.

So the question is: is the score given by this system correlated with candidate quality? I don't think this post gives enough data to know.

So the logical solution is for candidates to submit multiple applications with slight variations to their contact info, "John Schmidt", "John J. Schmidt", "John J. J. Schmidt", "John Jacob J. Schmidt", "J. J. Jingleheimer Schmidt", etc.

It's a good day to have 3 middle names.

Hey, that's my name too!

    Whenever I send them out
    The filters always route: 
    "Spammer: John Jacob Jingleheimer Schmidt"
    [N/A] [N/A] [N/A] [N/A]

If you have no requirements for accuracy, you can just advance 35% of applicants at random.

If the first 50 people who apply are all bots, why are you reading resumes in order of submission?

One of the first things you do when hiring is to set a period and randomize order of resume when reviewing because early application is not a strong signal.

Sounds like you're pretty bad at hiring pipelines.

I wonder if you could solve this for programming specifically as follows:

1. Give them some easy leetcode questions. Nothing that a competent programmer would have any problem with.

2. If they pass, ask for a deposit of like $20. Shouldn't be an issue for people who are actually serious.

3. Do more simple leetcode questions but this time on zoom so you can tell if they are using AI. If they pass that they get the deposit back.

(Yeah I know there are real-time interview cheat AI programs but based on what I've seen on demos of them it's super obvious when they're being used.)

Probably not practical but just a thought!

This selects for desperation.

I'm not going to do any of those 3 things for a would-be employer.

They don't seem like unreasonable things to me so I guess it also helps filter out unreasonable people!

Number 2 is so unusual I would immediately flag the company as a scam. How is asking for money a reasonable approach? There are alternative ways, easier too depending on country, and all you'd be doing is selecting for desperation while also spreading bad rumors from all the people who nope out when they see such malpractice.

Yeah it's unusual. Nobody does this. It was an idea.

What are the alternatives if you are getting over 100 applicants an hour? If you have genuine ideas I'm curious.

I have a fair amount of code available via open source repositories. As in the kind that real people actually use, not just up on my personal GitHub.

If someone is going to ask me to do leetcode problems it means they haven't done their research on me. Why would I want to work for them if they won't spend the time to do this? Digging into my publicly available background should be enough to tell them I am or am not of the caliber they're looking for in terms of hands on keyboard development.

there have got to be better ways to optimize pipelines. maybe set a limit on number of applications for a role based on the number you/your team can reliably go through them. if more are needed then open the role for another wave of applications.

Except the bit about ranking a decades long S3 engineer lower than an intern with GitHub repo.

Are you suggesting using AI/Agents to apply for roles?

Reply on news.ycombinator.com