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 Engineer

Remedyrobotics · San Francisco (hybrid)

About Remedy Robotics

Cardiovascular disease is the #1 cause of morbidity and mortality in the world. Much of this could be prevented with better access to specialist care. Take stroke as an example: any delay in treatment can lead to permanent disability or death. However, due to a lack of specialist surgeons, the most effective intervention can only be performed in 2% of US hospitals. For patients who present to one of the 98% of hospitals that do not offer the surgery, treatment is either significantly delayed or not offered at all because timely transfer is not feasible.

Our mission is to bring state-of-the-art vascular intervention to anyone, anytime, regardless of their location. Our team of medical clinicians, roboticists, and machine learning experts are working to bridge this gap by building the world’s first remotely-operated, semi-autonomous endovascular surgical robot.

We’ve already done what nobody else could—using our system, doctors from around the world were able to remotely perform this procedure from as far as 8000 miles away. We have now successfully performed first-in-human cases, including a remotely operated procedure, demonstrating the potential of our technology to revolutionize access to life-saving interventions. We now need your help to bring this technology out of the laboratory and into hospitals everywhere.

The Role

We’re looking for someone to continue leveraging our vast trove of medical imaging data in order to train and deploy deep neural network models. These models enable our surgical robot to understand and reason about both our robot and the patient’s anatomy, which ultimately gives doctors the insight and control necessary to quickly and safely complete the procedure.

You Have

  • At least 2 years of machine learning engineering experience (level will be commensurate with your experience)

  • Experience developing high-quality software, ranging from design and implementation to testing and deployment

  • Expertise with Python

  • Experience training image-based deep neural networks, including

    • Deep neural network libraries such as PyTorch

    • Defining training and validation datasets

    • Using data augmentations during training

    • Selecting loss functions and metrics

    • Cloud-based data and training

    • Conducting large-scale experiments to determine actionable improvements

  • Eagerness to learn on the job, iterate fast, and collaborate

Nice to Haves

  • Experience developing and deploying neural networks for physical systems, such as robots and autonomous vehicles

  • Experience with medical imaging data such as x-rays, CTs, and MRIs

  • Experience bridging the sim-to-real gap

View full listing