Picture this:
A Resume bullet says: "built X system that served 1M+ users at "
A job description says: "implement metrics, logging, and dashboards."
The AI sees no overlap and disqualifies the candidate. But any engineer who shipped to a million users (heck anyone who has built anything for any business) has absolutely built observability infrastructure, they just didn't waste resume space saying so. Sometimes they handcrafted the dashboards, sometimes they used already present tooling like Mixpanel and other observability platforms with built in dashboarding capabilities. The point is businesses require observability period.
Observability is just one example. Testing, code review, documentation, incident response, none of it makes the resume because those are all implementation details of the role, the meat and the potatoes are the ✨results✨
Of course that's not to say those implementation details are unimportant, but time and place for everything, they're meant to be communicated verbally during the interview. That's what interviews are for.
Resumes are meant to showcase highlights of your career through story telling focused on impactful work, not serve as a laundry list of every single task you've done. That'd turn it into an unsustainably large alphabet soup. A soup so large even the AI systems ingesting it would lose their appetite.
Of course I don't believe we'll ever give up AI in recruiting or any other industry. The AI isn't the problem. The prompt behind it is, and that's on whoever built the filtering tool. A productive step may be to work towards more intelligent system prompts that do more than just keyword matching. That's what LLMs are for after all. Here's an example of a system prompt designed to infer implied skills from context clues. Things like treating "served 1M+ users" as an observability signal, or treating quantified improvements as evidence of instrumentation.
Disclaimer: this is illustrative, not a comprehensive solution.
You are a resume intelligence system. Your job is to evaluate a candidate's
true qualifications by reading between the lines, not just scanning for keywords.
Experienced engineers don't list every task they performed. They tell a story
of impact. Your job is to understand what that story implies.
When evaluating a resume against a job description, consider what the candidate
almost certainly did but didn't write down. A system serving millions of users
implies observability. Quantified improvements imply instrumentation. Enterprise
experience implies process. Real-time features imply distributed systems thinking.
Startup experience implies ownership and autonomy.
Use context clues from company size, scale, tech stack, and career trajectory
to infer unstated but implied competencies. Distinguish between a real skill gap
and a resume brevity gap. Flag the latter for interview validation rather than
automatic disqualification.
The best resumes are highlight reels, not task logs. They're narrative-driven
stories of impact, not exhaustive records of everything someone has ever done.
Don't penalize candidates for writing a good one.
Top comments (0)