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

Loop · San Francisco, CA

About Loop

Loop is on a mission to unlock profits trapped in the supply chain (https://loop.com/article/unlock-profit-trapped-in-your-supply-chain) and lower costs for consumers. Bad data and inefficient workflows create friction that limits working capital and raises costs for every supply chain stakeholder.

Loop’s modern audit and pay platform uses our domain-driven AI to harness the complexity of supply chain data and documentation. We improve transportation spend visibility so companies can control their costs and power profit. That is why industry leaders like J.P. Morgan Chase, Great Dane, Emerge, and Loadsmart work with Loop.

Our investors include J.P. Morgan, Index Ventures, Founders Fund, 8VC, Susa Ventures, Flexport, and 50 industry-leading angel investors. Our team brings subject matter expertise from companies like Uber, Google, Flexport, Meta, Samsara, Intuit, Rakuten, and long-standing industry leaders like C.H. Robinson.

 

About The Role

Loop is growing its AI team and you’ll have the opportunity to build both AI models and features that directly impact Loop’s business. Loop is positioned to disrupt the incumbent freight audit and pay market, and is one of the companies leading the AI services wave, utilizing AI to automate complex back-office workflows and tasks. You will face and solve many complex technical challenges while you receive guidance and feedback from the team. The range of work here is broad, you can work on everything from training and deploying in-house multimodal LLMs, scaling our inference infrastructure, or building out and shipping AI agent workflows. In doing so, you’ll have the opportunity to define how the AI and broader Loop team will grow. 

What you will work on

Our primary focus has been on document extraction and understanding, where we utilize multimodal LLMs, including our own in-house foundation model, to extract, normalize, and link data together into our domain model. As Loop’s customers rely on Loop to ingest and normalize highly accurate data, we hold ourselves to a high standard to build models with a very high level of accuracy. In tandem, Loop’s machine learning platform requires a high degree of reliability and scalability, and we expect our training and inference volume to scale several orders of magnitude in the coming year. Going forward, Loop will expand its AI capabilities, expanding into other areas such as workflow automation and audit, where we will utilize agents to tackle these problems. 

This role spans multiple domains:

  • ML modeling – training, evaluating, and deploying models.
  • AI engineering – utilizing and orchestrating API LLM models to solve business problems at Loop.
  • Backend engineering – building out atomic tasks, general backend work in the servicing or automation domain.

Some projects you might work on:

  • Building the next generation of our foundation model to scale document extraction to multiple languages as Loop scales internationally.
  • Developing agents to audit freight invoices and ingest long contracts.

Qualifications

  • 1+ years of hands-on experience in deep learning frameworks (e.g., PyTorch, Tensorflow, etc.)
  • Solid understanding of fundamental ML algorithms, especially multimodal LLMs.
  • Experience fine-tuning LLMs and deploying them to production or building out agentic systems.
  • Ability to ship high-quality code to production.
  • Experience in cloud environments (AWS, Google, Azure).
  • Strong communication skills and willingness to collaborate in a cross-functional team environment with domain experts.
  • Keep up to date with the latest AI research.
  • Ability to work in a fast-paced environment.

Bonus Points

  • Prior startup experience.
  • End-to-end experience building out machine learning models and features.

Compensation

  • Base pay 130k - 200k

Benefits & Perks

  • Premium Medical, Dental, and Vision Insurance plans
  • Insurance premiums covered 100% for you
  • Unlimited PTO
  • Fireside chats with industry leading keynote speakers
  • Off-sites in locales such as Napa and Tahoe
  • Generous professional development budget to feed your curiosity
  • Physical and Mental fitness subsidies for yoga, meditation, gym, or ski memberships