How to Make Your Resume ATS-Friendly in 2026
By Awais · · Updated
ATS-friendly means machine-readable, not high-scoring. The fixes that matter are about text extraction, and you can check every one of them in your own PDF.
How to Make Your Resume ATS-Friendly in 2026
"ATS-friendly" has been sold as a scoring problem for years. It is not. It is a text extraction problem, and that is good news, because extraction is something you can verify yourself in about a minute.
Worth clearing up first: applicant tracking systems do not sit there rejecting people. Filters exist, but a human configures them, and most reviewers say they do not use automatic ones. We covered the evidence for that in does an ATS score predict interviews. This guide assumes you have accepted that and want to know what genuinely matters instead.
What matters is this: before anyone reads your resume, software converts your file into fields. Name, contact details, employers, titles, dates, skills. When that conversion goes wrong you are not filtered out. You are stored incorrectly, which is worse, because you are now in the database as someone you are not.
The one-minute test
Open the PDF you are about to send. Not the editor, not the preview. The file.
Try to select your name with the cursor. Then your phone number. Then a bullet in your most recent role.
If the text highlights, a parser can read it. If it does not, your resume is a picture of a resume and no amount of formatting advice will help. This single check catches the most severe failure there is, and almost nobody performs it.
Then copy the whole document and paste it into a plain text editor. What you get is roughly what the parser gets. Read it. If your job titles have detached from their employers, or your dates are sitting in a strange order, you have found your problem.
What actually breaks extraction
Five things cause most of it.
Multi-column layouts. Text sits in a document in an order, and that order does not have to match what your eye does with the page. A two-column resume can extract as the entire left column followed by the entire right, which interleaves your skills into the middle of your work history. Some parsers handle columns well. You do not get to know which one is reading you.
Text inside tables. Tables are a layout tool that carries structure a parser will try to interpret. A role laid out across table cells can arrive with the title in one field and the employer nowhere.
Headers and footers. Contact details placed in the page header are the classic version of this. In some extractions the header region is dropped entirely, so the parser gets a resume with no way to contact you.
Images and icons carrying information. A skills bar, a rating out of five, a logo where the employer name should be. None of it survives extraction. If a fact only exists in a graphic, the fact does not exist.
Non-selectable text. Covered above. Exported as an image, scanned from paper, or flattened by an export setting.
Section headers a system will recognise
Use the ordinary words: Experience, Education, Skills, Projects, Certifications.
Parsers look for known headings to decide where a section begins. "Where I have made an impact" is a stylistic choice with a real cost, because the content underneath may not be classified as work history at all. If you want personality, spend it in the bullets rather than in the labels.
Keep one heading level for sections and one for roles. Nested or inconsistent heading structures make the boundaries ambiguous.
Dates and contact details
Write dates in a consistent, unambiguous format across every role. Pick one, such as Mar 2021 to Jun 2024, and never mix it with 03/21 to 06/24 elsewhere in the document. Inconsistent date formats are a common cause of roles being ordered wrongly or durations being misread.
Put contact details in the body of the document, at the top, as plain selectable text. Email and phone on their own line. Avoid making them images, avoid the page header, and avoid splitting a phone number across a layout boundary.
Keywords, honestly
Mirror the language of the posting only where it is true of you. If the posting says "incident response" and you have done incident response, use their phrase rather than your internal one. This helps a recruiter searching the database, which is the real mechanism, rather than fooling a score.
Do not paste a keyword list, and do not hide text in white. Reviewers find it, and it reads as an attempt to cheat rather than as evidence.
What matters less than you have been told
- PDF versus DOCX. Both parse well when constructed properly. Follow the posting's instruction. If it does not specify, PDF preserves your layout.
- One page versus two. No parser cares. Readers care about density, not page count.
- Fonts. Any standard font embeds and extracts fine. This is a readability decision, not a parsing one.
- The score a checker gives you. It measures that checker.
The checklist
Run this against the exported file, not the editor.
- Name, email, and phone all select as text.
- Copying the whole document into a plain text editor produces sensible order.
- Single column for the main content flow.
- No content that exists only inside a table, image, or icon.
- Nothing important in the page header or footer.
- Standard section headings.
- One consistent date format throughout.
- Terminology from the posting, used only where true.
How Lynt approaches this
Rather than scoring the text in the editor, Lynt renders the PDF you would actually export and reads it back the way a parser does, then reports what came out: extraction order, which section headers were recognised, whether contact details survived, how dates parsed. The output is a list of what did or did not extract rather than a percentage, because a percentage does not tell you which line to fix.
You do not need Lynt to do any of this. The copy-into-a-text-editor test above catches most of it for free.