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 .

ML Engineering Intern - Nebula

Symblai47 · United States

Elevating the quality of human life through every conversation

About the Team:

At Symbl.ai, we are a trailblazing force in the world of artificial intelligence, committed to pushing the boundaries of technology. Our latest breakthrough - the Nebula LLM - represents the cutting edge of innovation, and we're looking for dedicated Machine Learning Engineering Interns to be part of this journey.


About the Role:

As a Machine Learning Engineering Intern at Symbl.ai, you will immerse yourself in the dynamic world of Nebula, our pioneering large language model (LLM). You will work at the forefront of machine learning, tackling real-world challenges and developing innovative solutions that shape the future of AI and language technology.

Highlights of the ML Engineering Intern Role at Symbl.ai:

Join a collaborative, agile environment that fosters innovation and supports your growth as a machine learning professional. This is an exciting opportunity to work with large-scale, novel conversational data; and train and deploy foundational large language models from the ground up.


Working as a Machine Learning Engineering Intern, you will:

  • Collaborate on the development and enhancement of the Nebula LLM, our flagship product.
  • Dive into real-world data, exploring unique challenges in language technology.
  • Implement machine learning algorithms and techniques to solve complex problems.
  • Work closely with experienced professionals to gain insight into AI/ML research.
  • Engage in a variety of projects that directly impact our product's capabilities.
  • Gain practical experience in a collaborative work setting that encourages learning and personal development.

    Please Note: Although we have focused centers in Seattle, WA and Pune, IN, there are no restrictions on where you can be located for this role - Symbl is fully remote.  


To excel in this role, you should:

  • Hold a Ph.D. or Master's degree in a relevant scientific or technical field.
  • Have a strong foundation in machine learning, natural language processing, and deep learning.
  • Be proficient in programming languages such as Python, and exhibit familiarity with ML libraries.
  • Have experience working on large-scale ML and Deep Learning projects.
  • Have demonstrable prior hands-on experience and projects in relevant areas.
  • Demonstrate strong analytical and problem-solving skills.
  • Possess effective communication and teamwork abilities.
  • Show a passion for pushing the boundaries of AI and language technology.


About Symbl.ai

We are a venture-funded AI startup building conversational AI since 2018; and the journey of building safe, secure and business-ready AI to solve problems in communication experiences informs a lot of the decisions we make about how we build our technology. Symbl is a developer-first platform whose core mission is to bring understanding and generative AI to every business that  relies on understanding human conversations, and give machines the ability to comprehend communications better than humans. We believe this will transform how businesses think about their knowledge and will accelerate the various use cases where unlocking unstructured data for business use cases generates ROI at scale.

We obsess about a great developer experience for all our products, the business-readiness of the AI we build, and pride ourselves in bringing state-of-the-art Large Language Models (LLMs) to multi-modal multi-party conversations. 

As an organization, we firmly believe in equal opportunity and do not engage in any form of discrimination based on race, religion, national origin, gender, sexual orientation, age, veteran status, disability, or any other legally protected status. We are committed to maintaining a diverse and inclusive work environment where every individual is respected and valued for their unique contributions.

How to Apply:  Email with your cover letter including any relevant links to Github or your recent publications to [email protected]. We look forward to getting to know you!

 

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