Using tables or multi-column layouts — ATS reads left-to-right in a single stream, scrambling split designs into gibberish.
Missing contact fields — no LinkedIn URL or phone number can cost up to 10 points before a human even glances at it.
Submitting as an image or scanned PDF — most ATS systems cannot extract text from image-based files, resulting in a near-zero parse score.
Burying skills inside paragraph text — ATS parsers look for a dedicated Skills section with comma-separated or bulleted terms, not prose.
No quantified achievements — "managed a team" scores far lower than "managed a team of 8, shipping 3 features per quarter and cutting bug rate by 30%."
Generic soft skills only — "communication" and "teamwork" carry no ATS weight. Role-specific technical terms do.
Inconsistent date formatting — mixing "Jan 2022", "01/2022", and "2022" in the same resume confuses date parsers and gaps detection.
Objective statements instead of a summary — a dated objective line wastes prime keyword real estate that a modern professional summary would fill.
Using synonyms instead of standard titles — "revenue growth lead" instead of "Sales Manager" means ATS misses the role match entirely.
Non-standard section headers — "My journey" instead of "Experience" can prevent ATS from locating your work history at all.
Overly designed resumes with icons, logos, and photos — decorative elements add no parseable text and inflate file size without benefit.
One resume for every job — ATS scores drop sharply when keywords don't match the specific posting. Tailoring per role is not optional.