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 .

AI Engineer Specialist

Digibeeinc · São Paulo, Brazil (Remote)

About Digibee

Digibee is an iPaaS that scales integration workflows while reducing cost and technical debt. Rather than require specialized integration experts, Digibee lets every developer quickly build, test, deploy, govern, and monitor integrations across on-premise and cloud environments using a simple but powerful low-code interface.

Founded in São Paulo, Brazil, in 2017 and headquartered in Weston, Florida, our team is widely distributed throughout the Americas. In May of 2023, Digibee closed a Series B funding round of $60 million that is intended to drive our expansion in the United States.

 

About the role

We are seeking a highly skilled and innovative AI Engineer Specialist to join our dynamic team. As an AI Specialist, you will play a key role in designing, implementing, and advancing state-of-the-art generative AI models. The successful candidate will collaborate with cross-functional teams, contributing to deliver cutting-edge AI functionalities in Digibee Integration Platform.

 

On a typical day, you will…

  • Research, design, and develop innovative generative AI models and algorithms.
  • Implement and optimize deep learning architectures for generative tasks, focused on text generation.
  • Collaborate with cross-functional teams to define goals, requirements, and deliverables and implement the features.
  • Train and fine-tune models using large-scale datasets and advanced techniques.
  • Evaluate and assess model performance, making necessary adjustments to improve results.
  • Stay up-to-date with the latest advancements in generative AI trends and tools and contribute to the team's knowledge base.
  • Write clean, efficient, and maintainable code, following best practices and coding standards.
  • Document research findings, methodologies, and technical specifications.
  • Participate in code reviews and provide constructive feedback to peers.
  • Contribute to the development of tools and frameworks to facilitate generative AI research and development.
  • Interpret insights from our pre-sales, sales, customer success teams and clients to create AI-based features.

 

What you’ll need to bring

  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • Advanced or fluent English language proficiency.
  • Strong understanding of machine learning, deep learning, and generative models.
  • Proficiency in Python, TensorFlow, PyTorch, and other deep-learning related tools.
  • Familiarity with the Hugging Face Transformers library of pre-trained models, including GPT-2, BERT, and others.
  • Experience developing and training deep learning models using large-scale datasets.
  • Solid understanding of neural network architectures (CNN, RNN, LSTM, Transformers), optimization techniques, and loss functions.
  • Familiarity with natural language processing (NLP) tools and frameworks.
  • Strong problem-solving skills and ability to think creatively.
  • Excellent communication and collaboration skills.
  • Ability to work independently and manage multiple projects simultaneously.

 

Nice to have

  • Proven experience on projects involving Large Language Models (LLMs) such as GPT, Bard, or LLaMA.
  • Familiarity with data engineering and MLOps
  • Experience working with cloud platforms dedicated to training and deploying AI/ML projects such as SageMaker or Vertex AI.
  • Master degree in Data Science related fields.
  • Familiarity with graphs and GNN.

 

Location

Brazil / Remote

 

Our perks and benefits

  • Health care 
  • Dental care
  • R$ 1.200,00/month on Caju card (for food and meal allowance, mobility, home office supplies, culture, health, and education)
  • Life insurance
  • Child care assistance
  • Wellhub (Gympass)
  • English course: we have a partnership for group classes for R$100 monthly
  • Global Equity Program

 

Our culture

We believe in a highly collaborative work environment in order to foster constant development and exchange between teams. We encourage learning, sharing knowledge, and using new technologies to create disruptive ideas - we want to create something great together!

At Digibee, we know it's our people who make the difference. We embrace and value diversity and are dedicated to encouraging a supportive and respectful culture in our community.

We are interested in every qualified candidate eligible to work remotely in the country of this job posting. However, we are not able to sponsor visas for this position.