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 .

Senior Support Engineer

Ema · India - Bengaluru

Who are we?

Ema is building the next generation AI technology to empower every employee in the enterprise to be their most creative and productive. Our proprietary tech allows enterprises to delegate most repetitive tasks to Ema, the Universal AI employee. We are founded by ex-Google, Coinbase, Okta executives, and serial entrepreneurs. We’re well-funded by the top investors and angels in the world. Ema is based in Silicon Valley and Bangalore.

Who are you?

You are a Senior Support Engineer who delivers world-class post-deployment support for product and solution deployments. You are highly customer-oriented, calm under pressure, and excellent at triaging complex issues, reproducing problems, and driving cross-functional resolution with Implementation and Engineering teams.

You act as the customer’s advocate while ensuring issues are tracked, prioritized, and resolved with clarity, urgency, and high quality.

Roles and Responsibilities

  • Own customer-facing incident management and escalations for post-deployment enterprise accounts

  • Triage incoming issues by gathering context, logs, steps-to-reproduce, and customer impact

  • Create clear, actionable bug reports and support tickets with detailed reproduction steps and diagnostics

  • Coordinate closely with Implementation and Engineering teams to drive timely issue resolution

  • Track and manage SLAs and key support metrics including FRT, TAT, AHT, and CSAT

  • Provide proactive, regular status updates to customers and internal stakeholders

  • Communicate effectively with both technical and non-technical customer teams during incidents and escalations

  • Identify recurring issues and contribute to improvements in documentation, runbooks, and support processes

  • Maintain and enhance internal knowledge bases, troubleshooting guides, and escalation playbooks

  • Validate fixes, coordinate customer confirmation, and support post-resolution follow-ups

  • Participate in escalation and on-call rotations as required

Ideally, you’d have

Experience

  • 4+ years of experience in Support Engineering, Software Engineering, or Technical Customer Support

  • Hands-on experience supporting AI/LLM-based applications in production environments

  • Experience configuring, deploying, or supporting AI agents, workflows, or ML systems

  • Exposure to prompt engineering and iterative improvement of LLM outputs

  • Experience running UATs or customer-facing evaluations for AI systems

  • Proven ability to manage complex enterprise customer issues independently

  • Familiarity with support metrics such as FRT, AHT, TAT, CSAT

  • Strong production troubleshooting skills, including diagnosing AI performance and recommending improvements to accuracy and business KPIs

Technical Skills

  • Strong understanding of LLM application architecture, including:

    • Prompting patterns (system/instructions, few-shot, structured outputs, function/tool calling)

    • Agentic workflows and orchestration concepts
      RAG fundamentals (chunking, embeddings, retrieval, reranking, context windows)

    • Evaluation concepts (golden datasets, offline/online evals, quality metrics, regression testing)

  • Ability to diagnose ML/LLM performance issues and recommend next-step experiments or mitigations (guardrails, routing, prompt adjustments, data fixes)

  • Proficiency with APIs and integrations: JSON, REST (and SOAP where relevant), auth basics (tokens, OAuth), and troubleshooting payload/schema issues

  • Good understanding of backend concepts that affect AI systems: latency, retries, timeouts, queues, rate limits, and distributed system failure modes

  • Ability to read logs and understand API request/response flows

  • Experience with ticketing, incident management, and customer communication tools

Soft Skills

  • Excellent communication skills, both written and verbal

  • Ability to work effectively with technical and non-technical stakeholders

  • High ownership and accountability for customer outcomes

  • Strong analytical and problem-solving mindset, especially under production pressure

  • Curiosity and willingness to continuously learn new GenAI and automation techniques

  • Collaborative team player who works seamlessly across Support, Engineering, Product, and Customer Success

  • Deep customer empathy, always prioritizing real business impact

Equal Opportunity

Ema Unlimited Inc. is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.

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