[jerrythegerbil]

> 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.

[ludicrousdispla]

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.

[koonsolo]

Do you expect human Recruitment to be deterministic?

[PufPufPuf]

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

[kyralis]

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.

[jerrythegerbil]

> 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.

[falsemyrmidon]

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

[sevenzero]

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.

[Brian_K_White]

This reasoning isn't.

[bagels]

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.

[aesthesia]

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.

[spike021]

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.

[IshKebab]

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!

[lowbloodsugar]

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