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 | BEES Personalization

Bees · Campinas, São Paulo, Brazil

 

About us

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

 

About AB InBev Growth Group

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well-known global beer brands like Corona, Budweiser, and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

 

About the job

We are looking for a highly skilled AI Engineer to join our team and lead the design and implementation of complex AI agents capable of reasoning, planning, and collaborating with humans and other systems. This role goes beyond building simple chatbots: you will be responsible for creating multi-agent architectures, integrating advanced LLMs, and ensuring robustness, scalability, and security in production environments.

You will work closely with product managers, data engineers, and security specialists to develop agent-based systems that handle real-world complexity at scale.

 

What you'll do:

  • Design, develop, and deploy multi-agent AI systems for reasoning, planning, and task execution.
  • Implement RAG (Retrieval-Augmented Generation) pipelines, using orchestration tools and frameworks 
    (LangChain, LangGraph, Azure AI Foundry).
  • Optimize model serving for low-latency, high-throughput inference using frameworks like KServe, 
    Triton Inference Server, or Ray Serve.
  • Build observability and evaluation frameworks to monitor agent reasoning, success rates, and failure cases.
  • Collaborate with ML and data engineers to integrate structured and unstructured data sources into agent workflows.
  • Apply security and alignment techniques (guardrails, prompt injection prevention, red-teaming) 
    to ensure robust behavior.
  • Work in a CI/CD environment (Azure DevOps, GitHub Actions, ArgoCD) for rapid iteration and reliable deployment.
  • Participate in architectural decisions involving distributed systems, GPU usage, 
    caching strategies, and memory management.

 

What you'll need:

  • Strong proficiency in Python (for prototyping) and C++/Go (for performance-critical components).
  • Proven experience with LLMs (OpenAI, Anthropic, Llama, Mistral, or similar).
  • Practical experience with agent orchestration frameworks (LangChain, LangGraph, Semantic Kernel).
  • Deep understanding of search and retrieval systems 
    (vector databases like Pinecone, Weaviate, FAISS, Milvus).
  • Knowledge of distributed systems and experience deploying 
    ML models in Kubernetes/AKS/EKS/GKE environments.
  • Familiarity with ONNX Runtime, TensorRT, or DeepSpeed for inference optimization.
  • Experience with public clouds (Azure, AWS, or GCP) and infrastructure as code 
    (Terraform, CloudFormation, or Bicep).
  • Understanding of AI security (prompt injection, data leakage, adversarial testing).

 

Nice to have:

  • Experience with multi-agent simulations (AutoGen, CrewAI, OpenAI Swarm).
  • Background in red-teaming and offensive security for AI models.
  • Knowledge of graph databases (Neo4j, ArangoDB) for agent memory and reasoning.
  • Publications, research contributions, or involvement in open-source projects related to LLM or agent frameworks.

 

What We Offer:

  • Performance based bonus*
  • Attendance Bonus* 
  • Private pension plan
  • Meal Allowance
  • Casual office and dress code
  • Days off*
  • Health, dental, and life insurance
  • Medicines discounts
  • WellHub partnership
  • Childcare subsidies
  • Discounts on Ambev products*
  • Clube Ben partnership
  • Scholarship*
  • School materials assurance
  • Language and training platforms
  • Transport allowance

*Rules applied

Equal Opportunity & Affirmative Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

The following fields are optional, but anticipate the information for your registration*.

Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com