I Stopped Optimizing My Resume. I Started Reverse-Engineering Job Descriptions Instead.

TL;DR: I analyzed thousands of software engineering job descriptions and realized that most resume advice focuses on the wrong problem. The issue usually isn't ATS software—it's relevance.

A few months ago, I was applying for software engineering jobs.

Like most developers, I had spent hours polishing my résumé. It had quantified achievements, modern formatting, links to projects, and every technology I'd worked with over the last few years.

I thought it was solid.

The results said otherwise.

I kept applying.

Rejections.

Ghosting.

An occasional interview.

Then more ghosting.

At first, I blamed the market.

Then recruiters.

Then ATS software.

Eventually I asked myself a different question:

What if my résumé wasn't bad? What if it simply wasn't relevant to each job?

That changed everything.

Developers measure everything—except resumes

As engineers, we measure almost everything.

• We profile slow APIs.
• We benchmark databases.
• We optimize Docker images.
• We monitor production latency.

But when it comes to resumes?

Most of us follow generic advice from blog posts written years ago.

• Keep it to one page.
• Use action verbs.
• Add keywords.
• Make it ATS-friendly.

None of that is bad advice.

It's just incomplete.

Nobody explains which keywords matter, why they matter, or how much they affect your chances.

So I decided to stop guessing.

Turning resumes into a data problem

Instead of reading career blogs, I started collecting software engineering job descriptions.

Backend.

Frontend.

DevOps.

AI.

Full-stack.

Startups.

Enterprise companies.

Eventually I had a dataset containing more than 2,000 job descriptions.

Then I built a parser that extracted:

• Required technologies
• Preferred skills
• Seniority
• Responsibilities
• Frequently repeated keywords
• Relationships between technologies

Once everything was normalized, interesting patterns started appearing.

The biggest surprise

Everyone talks about ATS optimization.

But after analyzing the data, I came to a different conclusion.

The biggest problem wasn't formatting.

It was relevance.

Imagine a company hiring for a backend engineer using:

• NestJS
• PostgreSQL
• Docker
• AWS

Now imagine receiving a résumé that spends half a page talking about:

• React animations
• Tailwind CSS
• Figma
• Mobile development

Those skills aren't bad.

They're just not what this company is looking for.

The strongest resumes naturally reflected the language and priorities of the job description without looking like keyword spam.

More keywords don't automatically help

Another interesting pattern was keyword stuffing.

Many resumes repeated technologies several times, hoping ATS software would rank them higher.

Something like:

React • React.js • React Developer • React Framework

Modern hiring systems are becoming increasingly semantic.

Just like search engines, they're getting better at understanding context rather than simply counting repeated words.

Showing where and how you used a technology is far more valuable than repeating its name multiple times.

Companies hire outcomes—not technology lists

This might have been my favorite realization.

Many resumes look like this:

JavaScript
TypeScript
React
Node.js
Docker
AWS
Redis
MongoDB

Compare that with:

• Reduced deployment time by 60%.
• Built APIs serving over 100,000 requests per day.
• Automated CI/CD pipelines.
• Improved application performance by 40%.

Both candidates may know exactly the same technologies.

Only one demonstrates impact.

Recruiters hire people who solve problems—not people who can list frameworks.

Why I built Talorr AI

Originally, this analysis was just for me.

I wanted a tool that could answer questions like:

• Which important skills am I missing?
• Which parts of my résumé don't match this role?
• Which keywords actually matter?
• How closely does my experience align with this job?

That eventually became Talorr AI.

Instead of simply rewriting resumes with AI, it compares your résumé against a specific job description, identifies missing skills, highlights weak areas, explains why they matter, and generates a tailored ATS-friendly version if you want one.

The goal isn't to make every résumé look the same.

It's to make every résumé more relevant.

What do you think?

I'm curious how other developers approach job applications.

• Do you customize your résumé for every application?
• Do you rely on AI tools?
• Have you noticed better results after tailoring your résumé?

I'd genuinely love to hear your experience.

If you'd like to see the tool that came out of this experiment, you can check it out at https://talorr.com/blog/how-to-tailor-resume-to-job-description.

I'm especially interested in feedback from recruiters and hiring managers—I'd love to compare your real-world experience with the patterns I found while analyzing thousands of job descriptions.