Before a person reads your application, software parses it, indexes it, and scores it. None of that requires tricks — it requires a document that survives parsing and answers the posting in its own vocabulary. This page is the whole method: six fundamentals, a four-step check before you send, and the questions people actually ask.
A parser reads the text layer of your document. A scanned page or an exported image has no text layer — the screener receives a blank application. If you can select and copy a sentence in your PDF, a machine can read it too.
Parsers look for the labels they were trained on: Experience, Education, Skills. “Where I’ve made an impact” reads well to a person and files your work history under nothing at all. Save the voice for the bullets, not the headings.
Parsers flatten a page into a single stream of text. A two-column layout interleaves your job titles with your skills list, and the result is nonsense — right words, wrong order. One column is the only layout that always survives.
A screener matching on “Kubernetes” does not credit “container orchestration”. If the posting names a technology and you have used it, name it the same way — the match is textual before it is semantic.
Screeners assemble a timeline from your employment dates. Mixing “2021–Present”, “Mar 22”, and “2023/06” forces the parser to guess, and a wrong guess reads as a gap or an overlap you never had.
Skill bars, icons, charts, and headshots carry meaning to a person and vanish for a parser. Anything you want credited has to exist as words on the page — decoration is at best ignored and at worst breaks the text around it.
Single column, standard section labels, real text. Copy the whole resume and paste it into a plain-text editor — what you see there is what the screener gets.
Read the posting once more and mirror its exact vocabulary for the skills you genuinely have. The work is already yours; the naming has to be theirs.
Lead bullets with outcomes and numbers, not duties. “Cut deploy time from 40 minutes to 6” outscores “responsible for CI/CD” with any reader, human or not.
Score the resume against the posting before you send it — the number, the missing keywords, and what to fix. That is exactly what the resume check returns.
An ATS — applicant tracking system — is the database a company runs hiring through; parsing your resume into fields is one of its jobs. AI screening is the newer layer on top: models that read the parsed text and score how well it answers the role. The fundamentals in this guide serve both — if parsing fails, nothing downstream ever sees your work.
No — and it ages badly. Invisible white-text keywords and skill lists padded with tools you have never used were tricks against naive matchers. Modern screeners read context, and a recruiter reads what the screener flags. The durable version of the tactic is legitimate: name your real skills the way the posting names them.
Either is fine if it is real text — the format matters far less than what is inside it. A clean single-column PDF exported from a text document parses reliably; so does a .docx. What breaks parsing is scans, text baked into images, tables used for layout, and multi-column templates.
The guide is the general case — what makes any resume readable and scoreable. The check is your specific case: your resume against one posting, with the score it earns, the keywords it is missing, and a rewrite aimed at that role. Fix the fundamentals here, then run the check on the job you actually want.
The check applies everything on this page to your resume against one specific posting — the score, the missing keywords, and a rewrite that grades higher.