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 .

AI Engineer

Vooma · San Francisco Office

About Vooma

Enterprise software is in the midst of a revolution. The world is shifting from "systems of record" to "systems of automation" - LLM powered agents and orchestrators that perform complex business tasks on behalf of and in concert with humans, with increasing levels of autonomy.

Vooma's mission is to build the AI orchestration platform for America's $800+ BN trucking industry. Everything we touch in the physical world is moved on a truck - it's the backbone of the US economy. Yet logistics today is still extremely manual, which makes it one of the hungriest industries for the shift toward AI Agents.

At Vooma, the products you'll build will help shippers, freight brokers, and trucking companies win more business and seamlessly execute freight. Our AI agents operate across voice, email, sms (and more) to automate critical workflows across the load lifecycle.

Vooma is backed by top-tier investors including Index Ventures, Craft Ventures, Y-Combinator, and CEOs, founders and executives from the logistics industry.

About the role

Your role, should you choose to join us, will be an AI Engineer on our founding team.

You're the right person for this role if you're excited to work at the frontier of applied AI, training and iterating advanced models that work in the real world. You’re driven by turning cutting-edge capabilities into reliable, high-performance systems that deliver highly scaled impact.

You're a great fit if you're known for going deep on model behavior and building systems that continuously improve through well-designed data flywheels. You find energy in shipping fast, learning from real usage, and iterating toward systems that feel almost magical in their effectiveness.

Your responsibilities will include:

  • Training, fine-tuning, and deploying state-of-the-art multimodal models across a range of real-world tasks

  • Designing and implementing evaluation frameworks to rigorously measure model performance and guide iteration

  • Building and scaling data flywheels - collecting, curating, and generating high-quality datasets to continuously improve model outcomes

  • Developing systems for live learning, feedback incorporation, and continuous model adaptation in production

  • Implementing techniques like negative mining to harden models against edge cases and failure modes

  • Owning the full lifecycle from experimentation → validation → deployment → monitoring

  • Collaborating closely across engineering and product to integrate models into reliable, high-performance systems

Must haves:

  • Hands-on experience working with modern foundation models (LLMs, multimodal models, or similar) in production settings

  • Strong intuition for model behavior, evaluation, and failure modes

  • Experience with fine-tuning, training pipelines, and dataset construction

  • Familiarity with techniques like RLHF, synthetic data generation, or active learning (not required, but highly relevant)

  • Comfort working across Python-based ML stacks and Typescript-based production systems

  • A bias toward action - you run experiments, measure results, and iterate quickly

  • The mindset of an owner: you care about outcomes, not just outputs, and push systems to actually work in the real world

Why You'll Love Working Here

  • Rare opportunity to join a formidable team building an enduring company

  • Direct impact - your work will be actively helping to food get on the table for millions of households

  • Cutting-edge technology - work with the latest in agentic AI using modern stacks (Next.js, GraphQL, Node, OpenAI, Anthropic)

  • Competitive compensation with significant equity upside potential

  • Comprehensive benefits including medical, dental, and vision coverage

  • In-person collaboration in San Francisco with a talented, experienced founding team

This role is based in person in San Francisco, CA (not remote).