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 .

ML Engineer

Willowvoice · San Francisco Bay Area

Overview

We're looking for a ML engineer with deep experience in ML models to help us deliver a world-class Willow experience.

Willow is a voice dictation app that lets you type anywhere on your computer using your voice. People already rely on Willow every day to write emails, respond in Slack, prompt AI, and move faster in their day-to-day work.

Our mission is to build the voice operating system, a way to control your computer entirely with your voice.

We're backed by the best, like Box Group, founders of Instacart and Hubspot, Y Combinator, and more.

The Opportunity

We’re at the very beginning of a shift in how people use technology.

The first major interface was the keyboard and mouse. The second was touch. The third is voice.

This is your chance to help build it.

You’ll be joining early, working on ML development. The impact is real. This is the kind of work that shapes how people use computers for decades to come. If we get this right, billions of people will use what we build every day.

The Role

We're looking for someone to be heavily involved with the development of our ML systems and models.

You'll:

  • Build and optimize Willow's ML infrastructure

  • Own model performance, reliability, and accuracy

  • Talk to users, understand their needs, and design ML solutions

  • Work with our founding team and designers to build a high-quality product

  • Stay current on ML tools, frameworks, and best practices

This is a high-ownership role where you'll shape both the product and the ML platform. You'll work closely with the team and move fast.

Your Background

We're looking for someone who:

  • Contributed to open-source ML projects / published research in RL or related fields

  • Deep understanding of ML principles and algorithms, including experience with RL fine-tuning and preference optimization (e.g. DPO)

  • Expertise in Python (2+ years)

  • Demonstrated ability to innovate in model development, addressing challenges like latency, hallucinations, context-awareness, and personalization.

  • High attention to detail and willingness to learn

  • Likes working directly with users and building from feedback

How to Stand Out:

  • 1–3+ years at a fast-growing startup

  • Worked on AI, real-time, or multimodal products

  • Research our product and show us how to improve before we even talk

  • Prove you're proactive and reach out

You’re a builder at heart. You like to move quickly, work through ambiguity, and care about doing great work.

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