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 Engineer, Optimization

Helm.ai · Remote - US

You will:

You will focus on research and development related to the optimization of ML models on GPU’s or AI accelerators. You will use your judgment in complex scenarios and apply optimization techniques to a wide variety of technical problems. Specifically, you will:

  • Research, prototype and evaluate state of the art model optimization techniques and algorithms

  • Characterize neural network quality and performance based on research, experiment and performance data and profiling

  • Incorporate optimizations and model development best practices into existing ML development lifecycle and workflow.

  • Define the technical vision and roadmap for DL model optimizations

  • Write technical reports indicating qualitative and quantitative results to colleagues and customers

  • Develop, deploy and optimize deep learning (DL) models on various GPU and AI accelerator chipsets/platforms

You have:

  • Proficiency in ML model development and optimization techniques (e.g. numerical optimization, quantization, sparsity, pruning, architecture search and design), particularly on model deployment onto GPU’s or AI accelerators

  • Strong understanding of deep learning algorithms, software engineering and GPU-based computing

  • Experience working with neural networks in Tensorflow and/or PyTorch

  • Proven ability to thrive in fast-paced environment

  • Ability to communicate complex technical concepts to colleagues and a variety of audience

  • Introspection, thoughtfulness, and detail-orientation

  • Proficiency in Python

The following are a plus, but not required:

  • Master’s or Ph.D. in a related field and/or 5+ years of experience in a directly related field

  • Computer vision experience

We offer:

  • Competitive health insurance options

  • 401K plan management

  • Free lunch and fully-stocked kitchen in our South Bay office

  • Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale

  • The opportunity to work on one of the most interesting, impactful problems of the decade

Helm.ai is proud to be an equal opportunity employer building a diverse and inclusive workforce. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.


Any unsolicited resumes/candidate profiles submitted through our website or to personal email accounts of employees of Helm.ai are considered the property of Helm.ai and are not subject to payment of agency fees.

View full listing