You're losing talent before you even talk to them. The irony? Your hiring process is designed to find great people—but it's filtering them out instead. Most CTOs don't realize their resume screening creates an inverse selection problem: it optimizes for the wrong signals while rejecting the engineers who'd actually move the needle.

Here's what that looks like in practice.

Sign 1: You're optimizing for keywords, not capabilities

Your ATS flags candidates with "kubernetes" and "microservices" on their resume. Sounds reasonable. Except the best infrastructure engineer I ever hired had neither term on his resume—because he'd been solving distributed systems problems at a startup using different tooling.

Resume keyword matching assumes skills are nouns, not verbs. It finds people who list technologies, not people who solve problems. The guy who copy-pastes framework names into his resume ranks higher than the one who rebuilt your entire deployment pipeline but called it "custom orchestration tooling."

You end up interviewing people who are good at resumes. Not good at engineering.

Sign 2: You're filtering by pedigree instead of performance

"Must have worked at a top tech company."

"Degree from a tier-1 university required."

These filters sound like quality bars. They're actually proxies—and bad ones. The engineer who scaled a payments system at a scrappy fintech startup probably learned more about resilience and tradeoffs than someone who touched one service at Google for 18 months.

Pedigree tells you where someone got opportunities. Performance tells you what they did with them. When you filter by the former, you miss the latter entirely.

The false negative trap

Strong engineers from non-traditional backgrounds get filtered out first. They don't have the brand names. They don't have the right degree. What they do have: ability to ship, debug production incidents at 2am, and make pragmatic tradeoffs under constraints.

These are your best hires. Your ATS never sees them.

Sign 3: Your "experience requirements" are arbitrarily high

"Senior engineer: 7+ years required."

Why seven? Why not six? Why not eight?

Most experience requirements are made up. They're meant to reduce volume, not predict success. But they backfire—because the best candidates for your role might have four years of intense, high-output experience that beats a decade of maintaining legacy code.

Years of experience measures time, not growth. You want the engineer who's been in the shit, not the one who's been around the longest.

What you're really trying to measure

You want judgment, taste, and decision-making under ambiguity. Someone who can look at a slow endpoint and know whether to add caching, rewrite the query, or rethink the data model entirely.

Resume screening can't measure that. So it uses "years of experience" as a stand-in—and filters out people who developed those skills faster.

Sign 4: Your process rewards professional resume writers

Formatting is flawless. Bullet points use action verbs. Every line is optimized for ATS parsing.

This resume looks great. It also tells you nothing.

The best engineers I know have mediocre resumes. They're too busy building to spend hours wordsmithing job descriptions. Meanwhile, average engineers hire resume coaches, optimize keywords, and make it past your filters—because they're better at gaming systems than building them.

You're not screening for engineering ability. You're screening for meta-skills that have nothing to do with the job.

Resume polish is negatively correlated with output

A beautifully formatted resume signals the candidate knows how hiring works. An unpolished resume from someone who shipped three features last quarter signals they were busy doing the work.

Guess which one your ATS promotes?

Sign 5: You're filtering out people who would pass your technical bar

The real test: put your resume-rejected pile in front of your best engineer for 30 minutes each. How many would they recommend moving forward?

If the answer is more than zero, your screening process is broken. You're not filtering out noise—you're filtering out signal that doesn't fit your template.

This happens when your screening criteria and your actual hiring bar diverge. The resume says "react, node, AWS." The job needs someone who can debug a race condition in your websocket service and propose a fix that doesn't break backwards compatibility.

Those aren't the same thing.

The uncomfortable metric

Track how many candidates you reject at resume stage who would've passed your technical interview. Most companies never measure this—because it would reveal the process is rejecting qualified people at scale.

The ones who make it through aren't the best engineers. They're the ones whose resumes happened to match your filters.

What this actually means

Resume screening optimizes for pattern matching, not capability. It finds people who look like what you think you want, not people who can do what you actually need.

The fix isn't better keyword lists or stricter GPA cutoffs. It's recognizing that resumes are weak signals—and building a screening process that measures the work itself, not the packaging around it.

Because right now, your best candidates aren't getting filtered out by accident. They're getting filtered out by design.