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 .

ML Engineer (AI)

Connecthum · US

We’re hiring an ML Engineer to work on AI evaluation, synthetic data, and model safety at scale.

The Company Behind the Role:

  • Working on enterprise-grade AI systems and large-scale deployments.

  • Strong expertise in AI, data, cloud, and analytics.

  • Operating across the US, Europe, and other global markets.

  • Projects focused on real-world AI applications and safety-critical systems.

Global technology consulting and engineering company with a strong focus on AI and data platforms.

Your Impact:

  • Build and scale automated evaluation systems for AI models.

  • Develop and train automated “judge” models to assess AI outputs.

  • Design validation frameworks to ensure accuracy and reliability across languages.

  • Create synthetic data pipelines to improve evaluation coverage.

  • Build scalable analysis and reporting systems for AI performance.

  • Work closely with language experts and cross-functional teams.

Your Superpower:

  • 3+ years of experience in ML engineering or applied ML research.

  • Experience training, fine-tuning, and evaluating language models or classifiers.

  • Experience building data pipelines and automated evaluation or monitoring systems.

  • Strong understanding of experimentation and statistical validation

  • Fluent English.

Tech Environment (High-Level):

  • Python.

  • PyTorch / TensorFlow.

Bonus Points If:

  • Experience with synthetic data generation.

  • Background in multilingual NLP.

  • Experience with distributed ML systems (Spark, Ray, cloud).

Why Join:

  • Competitive compensation

  • Comprehensive health coverage

  • Top-tier hardware & tools

  • Budget for learning

This role is open to candidates based in the US only. You must have valid work authorization. At this time, the company does not provide visa sponsorship.