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 (diffusion)

Genmo · San Francisco HQ

We are Genmo, a research lab dedicated to building open, state-of-the-art models for video generation towards unlocking the right brain of AGI. Join us in shaping the future of AI and pushing the boundaries of what's possible in video generation.

Role overview:

We are seeking an exceptional Research Scientist to join our team, focusing on developing cutting-edge diffusion models for text-to-video generation. In this role, you will be at the forefront of innovation, creating novel architectures and algorithms that transform written descriptions into stunning, coherent video content.

Key responsibilities:

  • Lead research initiatives in advanced diffusion models for text-to-video generation, focusing on improving visual quality, temporal consistency, and semantic fidelity

  • Develop and implement state-of-the-art algorithms for translating textual descriptions into dynamic video content

  • Design and conduct rigorous experiments to validate new ideas and evaluate model performance

  • Collaborate with cross-functional teams to integrate research breakthroughs into our production pipeline

  • Stay at the cutting edge of the field by regularly reviewing academic literature and attending top-tier conferences

  • Contribute to the research community through high-quality publications and open-source contributions

  • Mentor junior researchers and foster a culture of innovation within the research team

  • Work closely with product teams to align research directions with user needs and market opportunities

Qualifications:

  • Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field

  • Must have:

    • Strong publication record in top-tier conferences (e.g., CVPR, ICCV, NeurIPS, ICML) with a focus on generative models, particularly diffusion models

    • Extensive experience implementing and optimizing large-scale generative models for image or video tasks

    • Deep understanding of state-of-the-art techniques in text-to-image and text-to-video generation

    • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow

    • Excellent communication skills with the ability to explain complex technical concepts to diverse audiences

    • Proven ability to work collaboratively in a team environment

  • Ideal candidate will have:

    • Postdoctoral or industrial research experience in generative AI for video

    • Hands-on experience with text-to-video generation projects

    • Expertise in other generative model architectures (e.g., GANs, VAEs) and their applications to video

    • Experience working with large-scale datasets and distributed computing environments

    • Track record of successful collaboration with product teams on technology transfers

    • Familiarity with video codecs, compression techniques, and perceptual quality metrics

    • Contributions to open-source projects in the field of generative AI

Additional information

The role is based in the Bay Area (San Francisco). Candidates are expected to be located near the Bay Area or open to relocation.

Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.

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