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 .

Software Engineer, AI Inference

Skildai Careers · Pittsburgh, San Francisco, Bengaluru

Company Overview

At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.

Position Overview

We are looking for a Software Engineer to work at the forefront of deploying our cutting-edge AI models, enhancing the performance and capabilities of our embodied systems. You will be responsible for optimizing AI inference processes from lightweight to billion-parameter models, ensuring our robots operate with unmatched efficiency and intelligence in real-world environments. You will work at the intersection of systems and machine learning, directly contributing to making our AI models more powerful and adaptive by ensuring consistent performance in light of variable and perhaps unforeseen compute and hardware constraints.

Responsibilities

  • Develop and optimize runtime AI inference pipelines for real-world robotic deployment.
  • Build infrastructure, frameworks, and tooling to enable reliable integration of models into robotic systems and informative analysis of production models to drive the direction of architecture choice and deployment system design.
  • Formulate specialized optimization solutions for various inference paradigms and scenarios (autoregressive models, denoising models, hierarchical models, state machines, multi-agent systems, cloud-based inference).
  • Adapt optimization solutions to various compute, hardware, and networking constraints.

Preferred Qualifications

  • BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
  • Minimum of 3 years of industry experience.
  • Proficiency developing in low-level systems languages (C, C++, Rust, Go), Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
  • Deep understanding and practical experience with low-level systems concepts (multithreading, networking, embedded systems, memory management).
  • Experience with CUDA.
  • Deep understanding of state-of-the-art machine learning techniques and models.
  • Experience optimizing various machine learning architectures.
  • Experience with machine learning compilers.
  • Experience optimizing model inference for robotic systems deployment.

 

Base Salary Range
$100,000—$300,000 USD
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