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 .

QA and Test Engineer (12 Months Contract)

Fujitsu · Bengaluru

Job Title: QA and Test Engineer AI Data & Model Evaluation (12 Months Contract)

Company: Fujitsu Research

Location: Bengaluru
Team: MVP Engineering (MVPE), Fujitsu Research India
Experience: 25 years (flexible depending on aptitude) Employment Type: Direct Contract

Hybrid

Role Overview

We are looking for a smart, detailoriented QA Engineer who is passionate about AI quality, data validation, and model evaluation. In this role, you will assess the correctness of AI model outputs, analyse datasets, identify trends, and help ensure that customerfocused MVPs meet realworld expectations.
This position is ideal for someone strong in analytical thinking, data intuition, and product understanding—even if not deeply technical in coding.

Key Responsibilities

AI Model Testing & Evaluation

  • Validate predictions produced by AI/ML models (CV, NLP, LLMs, chatbots).
  • Design and execute test cases on internal testing platforms using “hard datasets”, edge cases, and adversarial inputs.

Functional and performance testing of AI Products (Beta version) on internal and customer datasets

  • Evaluate AI workflows using prompt engineering to test instructions, responses, and failure modes.
  • Provide structured, actionable feedback to engineering and research teams.

Data Quality & Analytics

  • Assess data quality, identify gaps, and flag anomalies in training and evaluation datasets.
  • Explore, curate, and validate datasets for customer-centric MVP development.
  • Perform trend analysis, descriptive analytics, and insights reporting for customer and internal teams.

Data Workflow & Annotation

  • Use data annotation tools to label or validate images, text, or multimedia datasets.
  • Work with internal data pipelines for ingestion, preprocessing, and quality checks.
  • Conduct web scraping or data collection for targeted tasks (basic Python/automation scripts is a plus).

Product Feedback & QA Processes

  • Act as the “first user” of AI prototypes and MVPs; provide usability and product behaviour feedback.
  • Maintain QA documentation, test reports, and evaluation scorecards.
  • Collaborate with engineers, product managers, and researchers to improve model performance and robustness.

Required Skills & Experience

  • 2–5 years of experience in QA, data evaluation, data annotation, or AI testing roles.
  • Accelerated or high velocity annotation through creative tools
  • Strong understanding of data workflows and basic ML concepts (classification, detection, NLP).
  • Familiarity with data annotation platforms (Label Studio, CVAT, Super Annotate, etc.).
  • Strong analytical, communication, and documentation skills.
  • Experience with web scraping, dataset exploration, and data validation.

Preferred Skills (Nice to Have)

  • Ability to design and execute structured test plans for AI/ML products.
  • Exposure to prompt engineering (LLM testing, red-teaming, persona-based prompts).
  • Basic coding knowledge (Python preferred) for data checks and scripting.
  • Understanding of evaluation metrics for CV/NLP/LLM systems.
  • Familiarity with modern AI tools like HuggingFace, OpenAI APIs, or vector search systems.
  • Experience in testing chatbots, multimodal models, or enterprise AI tools.

Who Will Succeed in This Role?

Someone who is:

  • Curious and detailoriented
  • Strong in pattern recognition and data intuition
  • Comfortable exploring new datasets and edge cases
  • Able to think like both a tester and a user
  • Passionate about AI quality and safety

Preferred Colleges- VIT, Manipal, SRM or other tier-2 or tier-3



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