A matching engine reads your CV differently from a human recruiter, but the good news is that the things which make a CV legible to a machine also make it clearer to a person. You do not need to stuff keywords or game anything — you need structure and concrete detail. Here is what helps.
Use clear, conventional sections
Standard headings — Experience, Skills, Education — let a parser map your content reliably. Creative layouts with text in columns, sidebars, or images often confuse extraction. A clean single-column document is easier to read for both us and a hiring manager.
Name your skills explicitly
If you used PostgreSQL, write "PostgreSQL," not just "managed our database." The engine can infer some skills from context, but explicit mentions are unambiguous. List the tools, languages, and frameworks you actually used.
Lead with outcomes, not duties
"Reduced page load time by 40%" tells us far more than "responsible for performance." Concrete, quantified results signal seniority and impact, both of which feed the experience factor in your match score.
Be specific about dates and titles
Clear start and end dates let us calculate years of relevant experience accurately. Vague or missing dates force the engine to guess, which can pull your scores in the wrong direction.
Do not over-optimise
You might be tempted to keyword-stuff for the algorithm. Resist it. Our model is built to understand genuine experience, and a stuffed CV reads badly to the humans who make the final call. Write for a person; the structure tips above are enough to make it machine-friendly too.