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 Machine Learning Engineer

Freenow · Berlin

Location: We are flexible! Come and join us either in Hamburg, Berlin, Barcelona or Athens

Freenow empowers smarter mobility decisions helping people to move freely and cities to thrive.

The Freenow marketplace is a real-time, two-sided platform connecting riders and drivers. Its efficiency and scalability are powered by a sophisticated ecosystem of Machine Learning systems spanning key domains such as Pricing, Allocation, and Marketplace Fraud.

In this role, you’ll help advance the state of the art of our Marketplace MLOps platform, working on impactful use cases that shape not only our business but also the daily experiences of our users.

Be ready to work in a multinational, diverse, highly motivated and collaborative team of passionate developers who strive for excellence and like to have fun.  Are you ready for your next ride?


YOUR DAILY ADVENTURES WILL INCLUDE:

  • Developing and maintaining high-performance, data-driven systems in a distributed infrastructure that impact our marketplace performance.
  • Streamlining online serving of traditional ML enabling faster A/B tests and reducing go-to-market 
  • Enhancing our MLOps platform capabilities to support cutting edge AI algorithms
  • Establishing ML Observability best practices enabling Self-healing system and automatic data drift detection
  • Implementing and optimizing solutions to some of our most business critical problems as part of a cross functional team consisting of Data and Software Engineering professionals.
  • Collaborating with other Machine Learning Engineers, Data Scientists and Data Platform Engineers to drive the state of MLOps in Marketplace and FreeNow.

Our Tech Stack: Python | Airflow | Java | Kubernetes | Kafka | Databricks | AWS & more


TO BE SUCCESSFUL IN THIS ROLE:

  • You are experienced in training, deploying, serving and observing Statistical/Machine Learning models on large-scale datasets in production.
  • You have experience developing and maintaining high throughput microservices in a production environment
  • You have experience with Model registry, experiment tracking ideally via MLFlow among other data manipulation and machine learning libraries
  • You excel as a team player, and an avid learner, with a proven capacity to collaborate seamlessly with diverse teams, including those in data, tech, and product, as well as other stakeholders.
  • You can communicate in English. We are a very diverse team with people from all over the world and English is our language of communication, besides Python.
  • You are passionate about harnessing Artificial Intelligence and have the will to play an essential role in the future of urban transportation.
  • Experience with streaming technologies and real-time data processing (e.g., Kafka Streams, Apache Flink) is a plus. 

BENEFITS & PERKS IN A NUTSHELL:

  • Flexible working arrangements
  • LinkedIn Learning
  • Sabbatical & special leave policies
  • WeRoad partnership
  • Birthday, 24th + 31st December off
  • Short term EU work policy
  • Mobility Credit
  • Health Insurance
  • Employee assistance program
 

Plus more local benefits depending on your work location!

 


DIVERSITY, EQUITY & INCLUSION:

FREE NOW is an equal opportunity employer and we consider qualified applicants regardless of race, religion, national origin, gender, gender identity, sexual orientation, disability or age.
We want you to grow and evolve, bring your true self to work


SEE WHAT OUR AWESOME COLLEAGUES SAY ABOUT US: