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 .

Senior ML/AI Engineer

Wizeline · Barcelona - Spain

Full Time True

We are:
Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.

With the right people and the right ideas, there’s no limit to what we can achieve

 

Are you a fit?
Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:

Key Responsibilities

  • Collaborate with data scientists and engineers to orchestrate LLMs and tools into complex AI workflows.
  • Implement optimized vector storage and indexing systems for NLP.
  • Develop tools and frameworks for prompt management, automated evaluations, and observability.
  • Monitor and improve agentic systems performance for accuracy and efficiency.
  • Provide ongoing support, troubleshoot issues, and implement updates for ML solutions
  • Build and maintain data processing pipelines for high volumes of structured and unstructured data.
  • Integrate third-party AI APIs (internal and external) to extend the functionality of systems.
  • Stay updated on GenAI, NLP, ML, and IR technologies, incorporating best practices and leveraging cloud infrastructure for efficiency.
  • Build and extend internal products on top of the LangChain ecosystem (LangGraph, LangSmith) to support prompt management, evaluation, and agent orchestration across the team.

Must-have Skills:

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field or equivalent work experience.
  • 4+ years of industrial experience in a machine learning engineering or data engineering role.
  • Strong programming skills in Python and/or another high-level language commonly used in machine learning.
  • Experience deploying LLMs, implementing automated evaluation pipelines (LLM-as-a-judge), and architecting multi-agent systems that utilize tool-calling and long-term memory to solve non-linear problems.

Nice-to-have:

  • AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows. 
  • Familiarity with cloud-based infrastructure and services (e.g., AWS and GCP), Docker, and the Git version control system
  • Familiarity with consuming and integrating APIs in a reliable and secure manner.

 

What we offer:

  • A High-Impact Environment
  • Commitment to Professional Development
  • Flexible and Collaborative Culture
  • Global Opportunities
  • Vibrant Community
  • Total Rewards

*Specific benefits are determined by the employment type and location.


Find out more about our culture here.