Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Senior Backend Engineer - Image Processing

Cloudinary · Kraków

Cloudinary is the image and video technology platform that enables the world’s most engaging brands to deliver transformative visual experiences at global scale. More than 2 million users and 10,000 customers, including Apartment Therapy, Bleacher Report, Etsy, Grubhub, Mattel, Mediavine, Minted, Paul Smith and Peloton, rely on Cloudinary to bring their campaigns, apps and sites to life. With the world’s most powerful image and video APIs backed by industry-leading artificial intelligence and patented technology, Cloudinary offers a single source of truth for brands to manage, transform, optimize, and deliver visual experiences at scale. As a result, the most engaging brands across all industries are seeing up to a 203% ROI using Cloudinary with benefits including faster time to market, higher user satisfaction and increased engagement and conversions. As a Senior Media Processing Backend Engineer, you’ll join Cloudinary’s Image team with both frontend and backend engineers. Together, we develop features, maintain existing code, and manage over 300 production cloud servers. The team is responsible for the core of Cloudinary’s main product, which generates over $100M ARR. This includes the image transformation engine and deep learning engines. These engines process 5 billion requests/mo (~2000 requests/sec). They serve the various Cloudinary applications. The world’s biggest brands are our customers. They rely on us to serve the media for the websites in a variety of modular transformations, high fidelity media, low bandwidth, and low latency.