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 .

Generative AI Engineer, Camera Algorithms

Apple · Beijing

Summary

Are you passionate about building groundbreaking camera technologies that enrich the lives of billions? Apple’s Camera Algorithms team develops the foundational technologies that define the photographic and cinematic experience for our users. We are the architects behind the image and video processing pipelines that bring every photo and video to life on all Apple products. We are seeking exceptional Generative AI engineers who share our passion for pushing the boundaries of computational photography and video. In this role, you will have the unique opportunity to leverage Apple's industry-leading hardware, including the Apple Neural Engine and our custom Image Signal Processor (ISP), to create features that redefine what's possible with a camera.

Description

As a member of our team, you will be at the heart of the innovation cycle, driving the future of Apple's camera capabilities. Your work will involve deep, cross-functional collaboration to deliver features that are seamlessly integrated and optimized.

Minimum Qualifications

BS and a minimum of 3 years of relevant industry experience Deep understanding of and hands-on experience with modern Generative AI models (e.g., GANs, VAEs, Diffusion Models, Transformers) and fine-tuning techniques (e.g., LoRA). Proficiency in ML frameworks such as PyTorch. Strong programming skills in Python and/or C++. Professional working proficiency in both Mandarin and English (written and verbal) is required for this role

Preferred Qualifications

Proven track record of innovation in Generative AI for image/video processing (e.g., Text-to-Image, Text-to-Video or Video-to-Video), demonstrated through shipped products or publications at top-tier conferences (CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, etc). Strong problem-solving, critical thinking, and communication skills. Experience as a key contributor to one industry-leading image/video generation model is a plus. Familiarity with camera sensors and ISP algorithms is a plus. Experience driving technologies from early-stage prototypes to polished, production-ready products is a plus.