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 .

Eloquent AI Fellowship Program

Eloquentai · San Francisco

Meet Eloquent AI

At Eloquent AI, we’re building the next generation of AI Operators—multimodal, autonomous systems that execute complex workflows across fragmented tools with human-level precision. Our technology goes far beyond chat: it sees, reads, clicks, types, and makes decisions—transforming how work gets done in regulated, high-stakes environments.

We’re already powering some of the world’s leading financial institutions and insurers, fundamentally changing how millions of people manage their finances every day. From automating compliance reviews to handling customer operations, our Operators are quietly replacing repetitive, manual tasks with intelligent, end-to-end execution.

Headquartered in San Francisco with a global footprint, Eloquent AI is a fast-growing company backed by top-tier investors. Join us to work alongside world-class talent in AI, engineering, and product as we redefine the future of financial services.

Accelerate Your AI Career at Eloquent AI
The Eloquent AI Fellowship is a high-impact, hands-on program designed for STEM PhDs, post-docs, and early-career AI researchers looking to transition into the world of applied AI. If you’re eager to develop, deploy, and optimize AI agents while gaining real-world experience in enterprise AI applications, this program is for you.

Through a structured, immersive experience, you’ll work alongside AI engineers, product managers, and researchers, gaining exposure to AI model development, MLOps, and conversational AI systems. You’ll leave the program ready to take on leadership roles in AI product development, engineering, or research.

You’ll also have the opportunity to collaborate with Prof. Aldo Lipani, Eloquent AI co-founder, whose pioneering research has advanced fine-tuning, evaluation methods, and simulators for LLMs. You’ll work on research projects involving AI evaluation, model robustness, and scalable testing frameworks—scientifically measuring, reproducing, and optimizing AI performance.


Program Structure

The Eloquent AI Fellowship is a 12-week rotational program providing in-depth exposure to AI engineering, applied ML, and AI-driven product management.

AI Engineering (6 weeks): Work on training, fine-tuning, and deploying AI agents that power enterprise-grade conversations. Gain hands-on experience in LLMs, RAG pipelines, prompt engineering, and inference optimization.

AI Product Development (4 weeks): Collaborate with AI product teams to design, iterate, and integrate AI solutions into enterprise applications. Learn how to bridge the gap between cutting-edge AI research and real-world impact.

Industry Applications & Capstone (2 weeks): Apply what you’ve learned in a real-world AI project, working with Eloquent AI’s product, engineering, and research teams to solve enterprise challenges.

You’ll be mentored by top AI practitioners and receive exclusive access to industry-leading resources, including AI speaker series, hands-on workshops, and networking with enterprise AI leaders.

You will:

  • Develop and deploy AI-powered agents, working with LLMs, RAG, and enterprise automation workflows.

  • Gain hands-on experience in AI infrastructure, including LLMOps, MLOps, cloud deployment, and model optimization.

  • Work on full-stack AI applications, collaborating with engineers and PMs to build scalable AI-driven products.

  • Translate AI research into practical applications, integrating the latest advances in language models, embeddings, and retrieval techniques.

  • Work directly with Eloquent AI’s leadership, learning from top AI engineers and product innovators.

Requirements

This program is open to postdocs, current or recently graduated PhDs, and MSc students in STEM fields with a strong interest in AI applications.

  • Current or completed PhD or MSc degree in Computer Science, Engineering, Mathematics, Physics, or a related field.

  • Strong mathematical foundation, particularly in statistics, linear algebra, and optimization techniques.

  • Programming experience, ideally in Python, with familiarity in ML frameworks like PyTorch and TensorFlow.

  • Interest in AI product development, data science, or machine learning engineering.

  • Ability to work in a fast-paced, collaborative AI-driven environment.

Bonus Points If…

  • You have experience with LLMs, NLP, or Retrieval-Augmented Generation (RAG).

  • You’ve contributed to open-source AI projects or published research in AI/ML conferences (NeurIPS, ICML, ICLR, NLP, SIGIR, etc.).

  • You have hands-on experience with LLMOps, MLOps, cloud-based AI infrastructure, or AI deployment at scale.

  • You have experience in AI strategy, product management, or business applications of AI.

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