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 .

Research Scientist Intern

Pluralis Research · Melbourne

Pluralis Research is pioneering Protocol Learning—a fully decentralised way to train and deploy AI models that opens this layer to individuals rather than well resourced corporates. By pooling compute from many participants, incentivising their efforts, and preventing any single party from controlling a model’s full weights, we’re creating a genuinely open, collaborative path to frontier-scale AI. If you want your work to shape the future of truly open innovation in artificial intelligence, join us.

Key Responsibilities

  • Contribute to groundbreaking research in Protocol Learning during your PhD. Join Pluralis for a 6-month research internship focused on publishing.

  • This fixed-term position offering an opportunity to author foundational papers in an emerging field with access to significant compute and focused mentorship from senior scientists.

  • Publication-Oriented Research: Conduct novel research within the domain of Protocol Learning, with the explicit goal of publishing in tier-1 ML conferences (NeurIPS, ICML, ICLR).

What We're Looking For

  • Publication Track Record: Current PhD candidate with at least one publication in top-tier ML venues (NeurIPS, ICML, ICLR, etc.).

  • Research Focus: Are working in a core technical area relevant to frontier models.

  • Technical Depth: Strong theoretical understanding of deep learning and distributed systems principles.

  • Implementation Skills: Proficiency in PyTorch and experience with large-scale training infrastructure.

FYI’s

  • We only hire in Australia and the United States. Visa sponsorship is limited to these countries.

  • Applicants must have professional-level English proficiency (written and spoken).

  • Pluralis is a remote team across Australia and the US. You’ll need to be comfortable working across timezones and collaborating with a diverse, distributed group.

  • Recruiters: we aren’t looking for agency support at this time. We’ll reach out if we need help.

Backed by Union Square Ventures and other tier-1 investors, we’re a world-class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We view the world as a better place if we are able to implement what we are attempting, and Protocol Learning as the only plausible approach to preventing a handful of massive corporations monopolising model development, access and release, and achieving massive economic capture. If this resonates, please apply.

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