ATS Glossary

Applicant Tracking System (ATS)

Software employers use to collect, parse, sort, and filter job applications before a human reviews them.

Common systems include Workday, Greenhouse, Lever, Taleo, iCIMS, and SmartRecruiters.

Parsing

Extracting structured fields (name, experience, skills, dates) from a resume PDF or DOCX into database fields.

Resume parser

The ATS component that reads your file and maps text into sections. Bad layout often breaks this step first.

Keyword matching

Scoring how closely resume terms match the job description’s skills, titles, and tools.

Boolean search

Recruiter queries using AND/OR/NOT to find candidates in an ATS or LinkedIn (e.g. Python AND AWS).

Rejection filter

Automated rules that drop applicants early-missing required skills, work authorization, or unreadable files.

Knockout question

Screening question in the ATS that can auto-reject (visa, location, years of experience, license).

Column layout

Two-column or sidebar resume design. Many parsers scramble order or skip sidebar contact and skills.

PDF vs DOCX

DOCX often parses cleanly. Text PDFs work; image-only or heavily designed PDFs frequently fail extraction.

Hard skills

Teachable, measurable skills (Python, Salesforce, CPA). ATS and recruiters search these heavily.

Soft skills

Interpersonal traits (leadership, communication). Useful for humans; weaker ATS match signals alone.

Job description match

How well your resume language mirrors the posting’s required and preferred qualifications.

ATS score

A tool-specific 0–100 style signal for parse quality and keyword fit-not a universal hiring cutoff.

Easy Apply

LinkedIn one-click apply. Your uploaded resume still lands in an employer ATS-format still matters.

Keyword stuffing

Repeating keywords unnaturally to game match scores. Looks spammy to humans and often fails to help.

Section headers

Standard titles like Experience, Education, Skills help parsers map content; creative titles confuse them.

Text box / floating text

Resume text placed in text boxes or shapes. Many parsers ignore or reorder that content.

Icons instead of text

Phone, email, or LinkedIn shown only as icons. Parsers need real text to fill contact fields.

Tables for experience

Using tables for jobs/dates often scrambles chronology. Prefer simple headings and bullets.

Headers and footers

Contact info only in header/footer may be skipped. Put critical details in the main body.

OCR / image resume

A scanned or design-export PDF where text is an image. ATS cannot reliably read it.

Canonical job title

A clear, standard title (Software Engineer) that matches how recruiters search-not only internal nicknames.

Skills taxonomy

Normalized skill labels ATS use for matching. Synonyms help humans; exact JD terms help machines.

Applicant profile

The structured ATS record built from your resume plus form fields (education, work history, skills).

Parse error / blocker

A formatting or structure issue that causes missing sections, wrong dates, or empty skill fields.

Workday

Enterprise HCM/ATS used by many large employers. Often strict on structured applications and uploads.

Greenhouse

Popular ATS for tech and growth companies. Parses attachments then stores structured candidate data.

Taleo

Oracle ATS common in large enterprises. Historically sensitive to complex resume formatting.

Lever

ATS focused on recruiting pipelines; still depends on readable resume text for search and matching.

iCIMS

Enterprise talent platform. Resume parsing quality still depends on clean text extraction.

SmartRecruiters

Talent acquisition suite with ATS features; uploaded resumes still need machine-readable structure.

Career page

Employer jobs site usually wired to an ATS-upload plus form fields create your candidate record.

Resume ATS checker

Tool that estimates parsing risk and keyword gaps before you apply (e.g. Jobkit / MyATSCheck).

Freemium unlock

Product pattern: free blockers preview first, then paid full score/keyword report-common in ATS tools.

NAP consistency

Name, address, phone consistency-more critical for local SEO than for national SaaS ATS tools.

E-E-A-T

Experience, Expertise, Authoritativeness, Trust-signals search and AI systems use when citing sources.

GEO (Generative Engine Optimization)

Making content clear, sourced, and structured so AI answer engines can cite it accurately.