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 .

Lead AI Engineer (Generative AI & LLMOps)

Somnio Software · Buenos Aires, Buenos Aires, Argentina

We are looking for a visionary Lead AI Engineer to architect and implement the generative intelligence core of our upcoming project. This is not a traditional research role; we need a "Builder" who understands how to turn raw model capabilities into reliable, scalable, and cost-effective product features.

As the Lead GenAI Engineer, you will design the RAG (Retrieval-Augmented Generation) architectures, select the appropriate model stacks, and ensure that our AI outputs are grounded, safe, and performant. You will work in lockstep with the Technical Leader to integrate AI services into the broader application ecosystem and mentor the team on AI engineering best practices.

Requirements

MUST

  • 8+ years of professional experience in Software Engineering, with at least 2 years of focused experience building and deploying GenAI-powered applications.

  • LLM Orchestration Mastery: Deep expertise in frameworks like LangChain, LlamaIndex, or Haystack for building complex chains and agents.

  • RAG Architecture: Proven experience implementing Retrieval-Augmented Generation, including chunking strategies, embedding models, and vector database management (e.g., Pinecone, or pgvector).

  • Advanced Prompt Engineering: Expertise in systematic prompt optimization, few-shot prompting, and Chain-of-Thought techniques to minimize hallucinations.

  • Model Integration & Selection: Deep understanding of the trade-offs between proprietary models (OpenAI, Anthropic, Gemini) and open-source models (Llama 3, Mistral) including hosting via Hugging Face or vLLM.

  • Python Proficiency: Expert-level Python skills, including asynchronous programming and performance optimization for data-heavy workloads.

  • Evaluation & Observability: Experience setting up AI evaluation frameworks (e.g., RAGAS, TruLens, or LangSmith) to measure accuracy, latency, and cost.

  • API & Backend Integration: Ability to design robust APIs (FastAPI/Flask) that handle the non-deterministic nature of LLMs, including streaming responses and graceful error handling.

  • English C1: Ability to explain complex AI concepts (like temperature, top-p, or context windows) to stakeholders and non-technical clients.

Nice to have

  • Fine-tuning Experience: Practical experience fine-tuning open-source models (PEFT, LoRA, QLoRA) for specific domains or style-matching.

  • LLMOps & Deployment: Experience with automated deployment of AI models using tools like BentoML, Modal, or AWS SageMaker.

  • AI Security: Knowledge of LLM-specific vulnerabilities (Prompt Injection, data leakage) and mitigation strategies.

  • Multi-modal AI: Experience working with Vision-Language models or Audio-to-Text/Text-to-Audio pipelines.

  • Product Thinking: A strong sense of "AI UX"—understanding when a feature should be an agentic workflow versus a simple deterministic function.


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