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 Software Engineer

Finout · Tel Aviv

Cloud is now one of the biggest business expenses—and one of the hardest to manage.
At Finout, we’re not just shedding light on spend—we’re giving companies the power to make smarter, faster, and more strategic decisions about the cloud.
We’re trusted by brands like The New York Times, Wiz, Elastic, SiriusXM, and Lyft, and backed by top-tier investors with over $85M raised. In just 4 years, we’ve grown to 100+ people across Tel Aviv and New York—and we’re just getting started.
If you’re looking to build something big, solve real problems, and grow fast—we’d love to meet you.

Responsibilities:

  • Own, enhance, and improve Finout’s core platform

  • Build and maintain systems for collecting and processing metrics from client environments

  • Focus on system efficiency and resilient infrastructure

  • Integrate well-known SaaS platforms into Finout’s big data repository (e.g., major cloud providers, Datadog, Snowflake)

  • Optimize Spark and Airflow processes

  • Contribute to data design and platform architecture while working closely with other business units and engineering teams

  • Face the challenges of testing and monitoring large-scale data pipelines

Requirements:

  • 7+ years of experience developing and operating large-scale, high-availability systems

  • 7+ years of experience with Python (Experience with Go is a plus)

  • Experience working with cloud environments (AWS preferred) and big data technologies (Spark, Airflow, S3, Snowflake, EMR)

  • Familiarity with metrics systems (e.g., Prometheus, cloud monitoring APIs) or time-series data – a strong plus

  • Autodidact, self-motivated team player with strong communication skills and a passion for solving challenges at scale

Our Values


We're a hybrid company with a big vision and a startup soul. If you’re excited to help shape the future of cloud infrastructure and join a team that cares deeply about what (and how) we build—we’d love to meet you.