We asked engineering managers what actually moves a resume to the “yes” pile this year. AI fluency mattered less than we expected.
Job-search advice ages fast, and this year’s version leans heavily on “learn AI tools or get left behind.” That’s true, but incomplete. We talked to a handful of engineering managers about what actually gets a resume a second look right now, and AI fluency was rarely the deciding factor.
It’s not just AI fluency
Nearly every candidate can now claim they “use AI tools daily,” which means the claim alone has stopped being a differentiator — it’s assumed.
What managers said they’re actually screening for underneath that is whether a candidate can tell the difference between code that merely works and code that’s actually correct, maintainable and safe to ship.
What actually stands out
The strongest signals mentioned again and again: a candidate who can clearly explain a decision they made and the trade-off behind it, evidence of having debugged something genuinely difficult (not just built something new), and any track record of making other people’s work better — through review, mentoring, or documentation.
None of these are new, and that’s exactly the point: fundamentals didn’t go out of style.
The interview signal that matters most
More than one manager mentioned the same tell: watching how a candidate reacts when their first idea is wrong. Do they defend it, or do they update quickly and try a different approach out loud?
That single moment tends to predict how someone will actually behave on a real team far better than any resume bullet point does.
A quick checklist
Before your next application or interview, be ready to talk through:
- One decision you made and why.
- One bug that genuinely stumped you and how you found it.
- One concrete way you’ve made someone else’s code or work better.
Small, specific stories consistently beat broad claims about tools and frameworks you’ve used.