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 .

GenAI Engineer

Tiger Analytics · Canada

Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI.

We are looking for a highly skilled GenAI Engineer with strong hands-on experience in building, evaluating, and deploying advanced Generative AI systems. The ideal candidate will have deep expertise in agentic frameworks, model fine-tuning, and reinforcement learning, along with a strong focus on experimentation, reliability, and hallucination mitigation beyond prompt engineering.

Requirements

  • Design, build, and deploy end-to-end Generative AI and agentic AI solutions for real-world use cases.
  • Develop and orchestrate multi-agent workflows using LangGraph, MCP (Model Context Protocol), and A2A (Agent-to-Agent) communication patterns.
  • Fine-tune large language models (LLMs) using supervised fine-tuning (SFT), RLHF, and other advanced techniques to improve task performance and alignment.
  • Apply reinforcement learning approaches to optimize agent behavior, decision-making, and long-horizon tasks.
  • Design and execute rigorous experimentation frameworks, including offline/online evaluations, A/B testing, and metric-driven improvements.
  • Implement robust strategies for hallucination reduction, such as retrieval augmentation, grounding, validation layers, confidence scoring, and self-reflection mechanisms.
  • Collaborate with data engineers, product managers, and platform teams to integrate GenAI solutions into production systems.
  • Monitor, evaluate, and continuously improve model performance, reliability, latency, and cost.
  • Stay up to date with the latest research and advancements in GenAI, agentic systems, and model alignment.

Required Qualifications

  • 5+ years of industry experience in software engineering, machine learning, or AI-focused roles.
  • Strong hands-on experience with LangGraph and building agentic workflows.
  • Practical experience with MCP (Model Context Protocol) and A2A (Agent-to-Agent) system design.
  • Proven experience in fine-tuning LLMs, including supervised fine-tuning and reinforcement learning-based methods.
  • Solid understanding and application of reinforcement learning concepts in production or research settings.
  • Strong background in experimental design, model evaluation, and statistical analysis.
  • Demonstrated ability to reduce hallucinations using techniques beyond creative prompting.
  • Proficiency in Python and experience with modern ML/AI frameworks.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

Originally posted on Himalayas