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 .

Machine Learning - Infrastructure

Causal · San Francisco

Our mission is general causal intelligence, AI that is capable of (1) predicting the future and (2) identifying the optimal actions to change that future.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because domains governed by physics have inherent cause and effect relationships, unlike visual or textual data.

Weather is the ideal training ground for an LPM. It is the most well-observed physical system, offering rapid, objective ground truth feedback from sensory observations and data at a scale that dwarfs what is used to train today’s LLMs.

Causal Labs is a team of researchers and engineers from self-driving, drug discovery, and robotics - including Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple - who believe general causal intelligence will be the most important technical breakthrough for civilization.

We look for infrastructure engineers who are excited to tackle unsolved problems.

Our training and inference challenges demand deep expertise in setting up distributed training clusters and optimizing performance for large models. If you have experience building large-scale ML infrastructure in related fields such as language and vision models, robotics, biology -- join us on this mission.

Responsibilities

  • Design, deploy, and maintain large distributed ML training and inference clusters

  • Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle

  • Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales

  • Analyze, profile and debug low-level GPU operations to optimize performance

  • Stay up-to-date on research to bring new ideas to work

What we’re looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

  • Strong grasp of state-of-the-art techniques for optimizing training and inference workloads

  • Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models

  • Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings

  • Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker)

  • Background working on distributed task management systems and scalable model serving & deployment architectures

  • Understanding of monitoring, logging, observability, and version control best practices for ML systems

You don’t have to meet every single requirement above.

View full listing