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 Senior Engineer - Graph

Able · Remote, LATAM

AI Senior Engineer

 

Our Story

Over the past several years, Able has grown immeasurably. We’ve also grown in the type of company that we are:

 

Chapter 1: We were founded in 2013 as a product and engineering hub for a portfolio of early-stage start-ups. We grew up as an in-house/external hybrid shared services model. That allowed us to hone our skills and establish our operational and cultural foundation.

Chapter 2: In 2019 we began to expand our vision. We began to grow outside of our inset partner base. We had good initial success meeting new partners, kicking off new relationships, and delivering high-value work.

Chapter 3: In 2023, we moved into the next phase of a new chapter, an expansion of the ambition of Chapter 2. Our strategy for growth centers around two audiences:

  • Venture Capital: VC firms are looking for trusted product and technology solutions to distribute seamlessly across their portfolios at scale.
  • Private Equity: PE firms are looking for trusted solutions that can catalyze growth for their portfolio companies at scale.


Chapter 3a: We are now in the next phase of Chapter 3, aligned to our mission and vision, and accelerated by the powers of applied AI. We believe that AI will be a powerful force in the end-to-end software development lifecycle. Specifically we are creating practices that – coupled with our world class talent – can deliver software significantly faster than legacy techniques. The result is increased value for our partners, who can dramatically increase the capacity of their product organizations. 

 

What you’ll be doing

We are seeking someone who views the Knowledge Graph not just as a database, but as a living organism that requires constant care, feeding, and pruning. You understand that a RAG system is only as good as the data underlying it. You are intrigued by the complexity of ingesting massive, messy datasets and transforming them into clean, connected knowledge.

In short, someone who likes:

  • Architecting Graph ETL: Designing and developing robust ETL pipelines specifically for graph ingestion. You aren't just dumping rows into tables; you are determining how disparate data sources connect, evolve, and relate in a graph structure.
  • Data Ingestion at Scale: Managing high-volume data streams using tools like Kafka and implementing CDC (Change Data Capture) patterns to ensure the graph reflects real-time reality.
  • Automated Graph Hygiene: Writing scripts and jobs for deduplication, orphan node detection, and data consistency checks. You take pride in a clean schema.
  • Modeling Time: Handling complex temporal relationships (e.g., how property ownership or financial status changes over time) within the graph.
  • Performance Tuning: Ensuring that as the graph grows (25k+ reports and beyond), the underlying query performance remains snappy through optimizing indexes and storage.

 

What we’re looking for

We want to work with people who have a passion for collaborating with their teams, building software while nurturing inclusive and respectful relationships with their coworkers. With the ones that are open about their shortcomings and what they do not know now, but remain eager to keep on growing and closing those gaps.

 

Ideally, they would also have:

  • Neo4j Expertise (Must Have): 4+ years of hands-on experience. You master Cypher, schema design, and the operational side of managing a production graph.
  • ETL & Pipeline Mastery: Strong background in building data pipelines. You know how to take raw data, clean it, and structure it for graph ingestion.
  • Streaming & CDC: Familiarity with event streaming platforms like Kafka and Change Data Capture methodologies to sync operational databases with the graph.
  • Python Proficiency: Strong Python skills for writing ingestion scripts, maintenance jobs, and custom graph algorithms.
  • Data Integrity Focus: Experience implementing automated jobs for entity resolution, deduplication, and quality assurance.

Nice-to-Have:

  • C# Knowledge: Ability to read or contribute to C# codebases.

Domain Experience: Prior work in Finance or Real Estate sectors.

Able's Values

  • Put People First: We're caring, open, and encouraging.  We respect the richness that we each bring into our work.
  • Imagine Better: We are optimistic in our outlook, as well as creative and proactive to deliver the highest quality.
  • Expect Excellence: We commit to each other to always strive to be our best.
  • Simplify to Solve: We create better outcomes by reducing complexity.
  • We are all Builders: We are motivated and empowered to help build Able, and our partner's businesses.
  • One Able. Many Voices: Our unity is our strength.  Our diversity is our energy.

Let’s build together.