An alarming number of people don't understand that LLMs work via purely stochastic processes, so I'm happy to see in-depth pieces like this. I'm looking for a job and maybe this is why it's so hard to get a callback these days: resumes are just dumped in some LLM black hole and no one really knows how it works. The author says:
> temperature 0.1 — low, supposedly nudging the model toward deterministic outputs
This is not correct (and is briefly touched on later in the piece when he sets temperature to 0), temperature is not some kind of "deterministic" switch, but rather it affects the sampling distribution (which becomes more "spiky"—but is still very much a distribution).
A distribution with all probability mass on one outcome is deterministic, so in principle, setting temperature to 0 _should_ result in deterministic outputs. There are a few reasons it might not, but I don't think any of these apply when running a local model like the author did.
Willing to be corrected but I believe this type of automated resume filtering is illegal. Not saying it never happens but my understanding is it is not typical.
I don't know for sure, but I would be surprised if it was illegal in my particular US state. You might be able to argue the AI has inherent biases that introduce illegal discrimination in the hiring process, but my understanding is winning I case like that would be very difficult, especially since most employers are very cagey about their hiring process and why they mades a decision.
In situations when you get hundreds of applications for one open position (real market now), whatever reduces your pool to the size a human can handle, works. You can preserve some diversity metrics in the process. This particular filtering is rather primitive, but LLM as a first filter can definitely do the job. You may burn less tokens than the hourly rate of your HR and it will be fairer than just dumping 50% of unread CVs in trash.
I'm a little confused, is this an ATS system that anyone actually uses? If not, I'm not sure how it's better than just asking ChatGPT to score your resume out of 100. Why would you want to optimize your resume for a system no one is using to score it?
> 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.
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.
> 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.
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...
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.
I thought this was only an old urban legend; some people actually use this technique? Especially in a trade supposed to be led by people trained in sciences?
I wonder how is this even legal? The only useful job the HR departments are ever required to do - they decide to automate it? Aside from being a daycare for adults, what exactly does HR accomplish? It's clearly NOT on the side of employees, but this seems like they're clearly NOT on the side of employers, either.
While resume's are being filtered left and right, they just make TikTok's on company's dime [1]. What a sad state of affairs.
This could be used as a good way to self-evaluate one's current position from the company's point of view. you would tweak prompts and guidelines that are expected from the company and see how you score
I sort of hope we land on 2 agents, one working for the candidate and one for the employee do a screen round. Salary compatiability could be negotiated by a 3rd party bot that knows both parties ranges and what would be needed each end of range, and figure out yes/no worth going ahead. Such a time saver.
Disregarding the fact that this thing is completely broken, its grading rubric is ridiculous to begin with (as was mentioned in the article itself, but I must reiterate how completely stupid this is):
> 35 points for open source contributions
> 30 for personal projects
I don't contribute to open source or have personal projects because I don't spend my free time doing what I do 40 hours a week to make a living. My 15 years of work experience is worth a maximum of 25%, so any company using this idiotic system would pass on me immediately. Open source and personal projects are fine, but in no sane world are they worth 65% of a resume's score.
They are selecting for people who are fine working in their free time. If you contribute to open source you are more likely to contribute to the company on weekends. If instead you have other hobbies or a family that takes up non-work hours you are more likely to drop your pen after forty hours.
Maybe they're selecting for intrinsic motivation. People who enjoy programming to the point they do it for fun, not just because it pays.
Free software work doesn't imply we work for free. We work on our projects, the stuff that we actually enjoy working on. Nobody is going to work on corporate products without adequate compensation.
You might have numbers on that but after working in a place with a strict no more than 40 hour policy my view is that people overwork for many reasons. Being an open source enthusiast is not one of them.
> temperature 0.1 — low, supposedly nudging the model toward deterministic outputs
This is not correct (and is briefly touched on later in the piece when he sets temperature to 0), temperature is not some kind of "deterministic" switch, but rather it affects the sampling distribution (which becomes more "spiky"—but is still very much a distribution).
I don't know for sure, but I would be surprised if it was illegal in my particular US state. You might be able to argue the AI has inherent biases that introduce illegal discrimination in the hiring process, but my understanding is winning I case like that would be very difficult, especially since most employers are very cagey about their hiring process and why they mades a decision.
Show someone a list of resumes with an "applicant score*" and they'll naturally ignore the ones with a low ranking
*scores are generated with AI, mistakes may be made, use only as a guide and verify results
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.
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 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.
Corpo bullshittery at its finest.
While resume's are being filtered left and right, they just make TikTok's on company's dime [1]. What a sad state of affairs.
[1] https://www.reddit.com/r/theprimeagen/comments/1tkgml2/bolt_...
> 35 points for open source contributions
> 30 for personal projects
I don't contribute to open source or have personal projects because I don't spend my free time doing what I do 40 hours a week to make a living. My 15 years of work experience is worth a maximum of 25%, so any company using this idiotic system would pass on me immediately. Open source and personal projects are fine, but in no sane world are they worth 65% of a resume's score.
Free software work doesn't imply we work for free. We work on our projects, the stuff that we actually enjoy working on. Nobody is going to work on corporate products without adequate compensation.
Now all my "non-work" time is spent on startup work. And none of that is visible via GitHub.