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 .

AI Research Scientist I

1910Genetics · Boston, MA

Company Overview 

We are the only AI-native biotech, pioneering small and large molecule therapeutics discovery by integrating massive multimodal data, frontier AI models, and high-throughput lab automation into an infrastructure for AI-enabled drug discovery. 

We hire top 1% talent to join our interdisciplinary team of scientists, engineers, researchers, operators, innovators, drug developers, business professionals, and technologists.  

Join us to build the world’s first AI infrastructure for tech-enabled drug discovery and to deliver a pipeline of diverse drug modalities for all major disease areas. 

As an AI Research Scientist I at 1910 you will be expected to roll up your sleeves as an Individual Contributor (IC) by keeping up with relevant scientific literature, prototyping promising methods, and contributing to our active drug design campaigns by applying 1910’s productionized AI/ML models.  

 

Role Description 

  • Propose and prototype AI and Machine Learning solutions that address use cases in 1910’s design pipeline  
  • Apply productized AI and Machine Learning models to advance 1910’s active drug design campaigns with the support of senior members of the AI Research Team  
  • Write and publish peer reviewed scientific articles with the support of senior members of the AI Research Team 
  • Periodically presenting recent AI research at internal journal clubs  
  • Learn how a molecule processes through the drug discovery process 
  • Work cross-functionally with scientific colleagues, being a subject matter expert in how AI and Machine Learning can be used to answer cheminformatics and bioinformatics questions  
  • Keep up-to-date on cutting-edge research in the AI for drug discovery space  

 

Qualifications 

  • PhD in a relevant discipline (Computer Science, Biology, Chemistry, etc.)