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 .

Software Engineer (Quantization Engineer)

Furiosa Ai · Seoul HQ

About Algorithm Team - Model Compression Part

LLM Quantization이 추론 효율성을 극대화할 수 있다는 점은 널리 알려져 있습니다. 그러나 이를 실제 서비스에 적용하는 것은 여전히 어려운 과제입니다. Model Compression Part는 사용자 친화적인 Model Compression 도구를 개발해 이러한 어려움을 해결하고, 고객이 자사 NPU를 최고의 효율로 활용할 수 있도록 지원하는 것을 목표로 합니다.

Model Compression 도구가 Hardware-specific 최적화를 포함할 때, 효율성을 극대화할 수 있습니다. 우리는 이러한 요구를 충족하기 위해 자사 NPU에 특화된 최적화 기능을 갖춘 자체 도구를 개발하였으며, 이를 통해 NPU의 성능을 최대로 끌어올릴 수 있는 필수 소프트웨어 스택을 제공합니다.

FuriosaAI Model Compression 도구는 자동화, 확장성, 안정성을 지속적으로 개선하면서 점점 더 많은 기능이 요구됩니다. 이에 따라 소프트웨어 엔지니어링 역량이 매우 중요한 상황입니다. 따라서 풍부한 소프트웨어 엔지니어링 경험을 보유하고 있으며, Model Compression 엔지니어로서 커리어를 발전시키고자 하는 인재를 찾고 있습니다.

Responsibilities

  • Model Compression 도구 개발

  • 다양한 양자화된 모델 확보 및 성능 검증

  • 이를 기반으로 더 진보된 Compression Algorithm개발

Minimum Qualifications

  • PyTorch 개발 경험이 풍부하신 분

  • 상용 소프트웨어 개발 경험이 있으신 분

  • 관련 분야에서 3년 이상의 실무 경력을 보유하신 분

Preferred Qualifications

  • DevOps 및 MLOps에 대한 경험과 지식

  • vLLM, TensorRT-LLM 등의 LLM inference tool을 사용한 경험

  • Deep Learning Quantization 경험과 지식

  • Deep Learning 가속과 관련된 회사에서의 근무 경험

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