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 .

Member of Technical Staff - Backend

Additiveai · San Francisco, California

About You

You are an exceptional builder who wants outsized responsibility and impact, and have a demonstrated history of thoughtful and pragmatic decision making. You have designed, built, and maintained backend systems that delivered on complex problems with simple and robust code. You love tackling novel challenges, you are adept at finding third party tools that meet our needs, and can both quickly make something work and design it to scale and handle edge cases. You’re genuinely passionate about building, and are excited to be an integral part of a team of like-minded folks.

Responsibilities

  • Build the core systems that deploy our machine learning capabilities both in training and production, build metrics systems for rapid iteration and introspection, and help improve the performance of both by using your judgement to optimize for evolving technical and product requirements.

  • Drive technology and architectural choices for Additive’s core services that will enable us to rapidly build and iterate on user experiences that enable our customers to do their best work.

  • Design and build the integration of ML inference, monitoring systems, Large Language Model (LLM) interactions, application layers, and tax-related business logic into our core systems.

  • Build out database layers supporting business logic and applications within our systems and data integration strategy with external vendor APIs and interfaces.

  • Collaborate with the rest of the team on effective CI/CD systems that helps us all move faster.

  • Engage directly with accountants to understand their needs and how we can best build a product that they absolutely love.

  • Be expected to operate with autonomy and judgement to deliver tremendous value while working with both technical and market constraints.

Requirements

  • 5+ years experience working on backend systems in a meaningful ownership role for what was built and why, ideally for systems with meaningful data or ML workloads.

  • Experience with ML concepts and integrating ML inference and monitoring capabilities into business applications.

  • Comfortable with Python, cloud providers such as GCP/AWS, container technologies (e.g. Docker, Kubernetes, serverless platforms), and database and storage layers (e.g. Postgres, SQL, S3/GCS, Redis).

  • We’re working fully in person 5 days per week in downtown San Francisco. Additive can cover relocation costs for moving to the San Francisco area.

At Additive we value diversity and are committed to an inclusive workplace. Additive is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. If you require an accommodation during the job application process, please notify [email protected] for support.

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