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 .

Research Scientist/Engineer

Astera · Emeryville HQ

About the Role

We are seeking brilliant, unconventional thinkers to join us. In this role, you will not be chasing incremental gains on standard benchmarks. Instead, you will be tasked with developing the foundational principles of Artificial General Intelligence. We believe that understanding the "code of the brain" is the most viable path to building truly intelligent machines, and we are looking for scientists who can bridge the gap between biological intelligence, computational theory, and building at scale.

Core Responsibilities

  • Fundamental research on brain-like world models: Develop and implement new architectures and learning algorithms for neuroscience-based world models. You will draw up on insights from deep learning, causality, probabilistic and energy based models, and biologically inspired architectures.

  • Scientific Discovery: Design experiments to test hypotheses regarding high-level cognition, causal representations, sensory-motor integration, active inference, and unsupervised learning.

  • Cross-Disciplinary Collaboration: Work alongside neuroscientists and software engineers to translate abstract mathematical frameworks into scalable systems.

  • Contribute to open science: Help innovate new publication models that incentivize speedy dissemination, open source code releases, free open access, and impact measurements based on uptake.

Desired Qualifications

  • Technical Depth: Preferred: PhD in Computer Science, Electrical Engineering, Neuroscience, Physics, or a related quantitative field. We are always open to considering exceptional candidates, even those with non-traditional educational histories.

  • Theoretical Rigor: Strong foundations in deep learning, graphical models, and information theory.

  • Coding Proficiency: Expert-level skills in deep learning frameworks (PyTorch/JAX), with a focus on clean, reproducible research code.

  • AGI Mindset: A demonstrated interest in the "big questions" of AI—robustness, generalization, and common-sense reasoning.

  • Curious about the brain: A genuine passion for exploring the computational and architectural principles of the mammalian brain.

  • Startup DNA: Ability to thrive in a lean, fast-paced environment where you have high autonomy and a direct influence on research direction.

Why Join Us?

  • We recognize the need for fundamental research: No pressure to align research with quarterly revenue goals, market competition, or product roadmaps. We can focus on solving the core problems and achieving breakthroughs that enable the next generation of AGI technology.

  • Mission-First: Your work contributes directly to the global pool of knowledge and the safe development of AGI.

  • Collaborative culture: We work in teams, organized around challenge problems and attack paths, without big-company inertia or politics.

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