Lever ATS: How Recruiters Actually Search for Candidates

Lever is the applicant tracking system behind hiring at many fast-growing technology companies. Airbnb, Shopify, Lyft, and thousands of companies at Series A through public stage use it. If you are applying to a growth-stage tech company and the application URL starts with jobs.lever.co, this is the system handling your resume.

Lever works differently from older enterprise ATS systems like Taleo or Workday. Understanding how is worth a few minutes before you submit.

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Search-driven, not score-driven

Older systems like Taleo assign your resume an automated score and use it to rank or filter candidates before a human ever looks. A threshold score, set by the employer, determines who advances.

Lever does not work this way. It parses your resume into structured fields and makes it searchable. Recruiters find candidates by searching the pool — by job title, skill, keyword, or experience. Your resume surfaces, or does not, based on whether those searches match what Lever extracted from it.

There is no automated cutoff score to beat. But if Lever misparses your resume, or if a recruiter searches for a term you did not include, you are invisible to that search regardless of how qualified you are.

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What Lever does understand: word stems

Unlike Taleo, Lever's search understands word stems. "Collaborate," "collaborating," and "collaborated" match the same search. "Manage," "managed," and "management" overlap.

This means you do not need to contort your writing to hit an exact keyword form. Past tense for past roles and present tense for current roles is fine — Lever will handle the variation.

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What Lever does not understand: abbreviations

Lever does not expand abbreviations to their full form. If a recruiter searches for "Search Engine Optimization" and your resume says "SEO," that search does not find you.

The fix is simple: write out the full term at least once, and include the abbreviation in parentheses if the role typically uses the short form. "Search Engine Optimization (SEO)" in your skills section covers both search variations.

This applies wherever abbreviations are common:

• "User Experience (UX)" rather than only "UX"
• "Project Management Professional (PMP)" rather than only "PMP"
• Technology names where the abbreviation and full name are both used in job postings

When in doubt, check whether the specific job posting uses the abbreviation, the full term, or both — then use both yourself.

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How Lever parses your resume

Lever extracts your resume into structured fields: name, current title, contact details, work history (company, title, dates, bullets), skills, and education. Recruiters can filter and sort by these fields directly.

The more conventional your resume structure, the more accurately Lever extracts it.

Section headings. Use standard headings that any ATS recognises: Work Experience, Skills, Education, Professional Summary. Non-standard headings risk content being misclassified or missed.

Skills section. Because recruiters filter directly by skills, this section is particularly important in Lever. A skill listed here is directly searchable in a way that a skill buried deep in a job description bullet is not. Keep it complete and specific.

Contact information. In the document body, not in a Word header or footer. Lever's parser, like most ATS parsers, can miss content placed outside the main body.

File format. DOCX parses most reliably. Text-based PDFs — exported from Word or Google Docs — typically work. PDFs from design tools like Canva, Figma, or Adobe Illustrator often produce partial or empty extraction because the text layer is not intact.

Single column layout. See the ATS-friendly resume format guide for the full formatting checklist that applies across Lever and other platforms. Two-column resumes produce garbled text extraction in Lever, as in most ATS systems. The parser reads across the full page width, mixing content from different columns into a single stream.

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What happens after you apply

When you submit through a Lever board, the recruiter sees two things: your uploaded resume and a structured profile that Lever auto-extracted. Active reviewing happens on the structured profile during filtering and search, and on the original document when a candidate looks promising.

Format for clean extraction first — so the structured profile is accurate — and for human readability second, so the original document reads well when someone actually opens it.

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The short version

Lever is more forgiving than Taleo on exact keyword form — word stems count, there is no automated cutoff score. But it relies heavily on recruiter search, which means abbreviation mismatches and incomplete skills sections will make you hard to find. Get your skills section right, spell out full terms alongside abbreviations, and ensure your resume parses cleanly as a single-column document.

PassATS identifies keyword gaps between your resume and a specific job description — including abbreviation mismatches — and produces a rewritten resume formatted for clean ATS extraction, downloadable as a .docx. For a step-by-step method on finding the right keywords, see how to find resume keywords for ATS.

Try it free — one full analysis, no credit card required