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 .

Member of Technical Staff, Science

Radicalnumerics · Menlo Park (US), Tokyo (JP)

Member of Technical Staff, Science

Location: SF Bay Area or Tokyo, Japan
Type: Full-time

About Radical Numerics  

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast multimodal biological datasets so that biological world models become a reality. Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools.

About the Role  

As a science-focused Member of Technical Staff, you will curate the multimodal biological datasets that power our models, analyze model behavior, and ensure our model outputs meet rigorous scientific standards. You'll co-develop benchmarks, filters, and validation pipelines with engineering peers so biological world models remain trustworthy and actionable.

What You'll Do 

  • Source, normalize, and steward large-scale genomic, epigenomic, transcriptomic, proteomic, and imaging datasets with rigorous metadata and provenance.  
  • Build evaluation suites and benchmarks that stress-test generative biological models across modalities and tasks.
  • Partner with AI engineers to analyze model outputs, run ablations, and surface insights that guide future architecture and training improvements.  
  • Integrate new datasets and annotations from external collaborators while maintaining compliance, privacy, and ethical standards.  
  • Communicate findings and best practices across Radical Numerics so teams can trust and act on model results.

What We're Looking For

  • PhD in genetics, computational biology, or a related field, OR demonstrated experience in biotech with a strong track record of impact over 3+ years.
  • Proven experience curating, harmonizing, and analyzing large biological datasets (e.g., genomics, single-cell, spatial, or imaging).
  • Fluency with Python, data tooling, and reproducible workflows (git, notebooks, containers).  
  • Ability to interrogate model outputs, debug unexpected behaviors, and translate findings into actionable recommendations.
  • Clear communicator who can bridge scientific context with engineering teams and partner organizations.
  • Curiosity and resilience when tackling open-ended scientific challenges.

Nice to Have 

  • Familiarity with generative model evaluation, red-teaming, or safety analysis in scientific domains.
  • Experience with statistical validation, quality control, or benchmarking for scientific or ML systems.
  • Experience building benchmarking frameworks or open datasets that became community standards.  
  • Contributions to shared analytics tooling or reproducible research pipelines.

Why Radical Numerics 

  • Help produce the multimodal biological world models that will power rapid detection, response, and countermeasures across global health.  
  • Collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes.  
  • Competitive compensation, comprehensive benefits, and support for continual learning.

How to Apply  

Send your resume, a brief note on why Radical Numerics resonates with you, and examples of relevant public codebases you’ve built. We review applications on a rolling basis.