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 .

Platform Engineer

Vectara · Remote US.

Platform Engineer

Remote (US)

Why Vectara?

Vectara's RAG and Agentic AI platform helps enterprises deploy AI agents and assistants that are accurate, secure, and explainable. We built the Hughes Hallucination Evaluation Model (HHEM) — #1 on HuggingFace with 5.5M+ downloads, cited in the New York Times and Visual Capitalist — and Mockingbird, a purpose-built LLM optimized for retrieval-augmented generation. Over 100 enterprise customers across high tech, defense, financial services, healthcare, and manufacturing trust our platform in production.

We're a ~50-person team backed by ~$70M in funding, founded by neural information retrieval and distributed systems experts from Google. Alumni of Cloudera, Splunk, MongoDB, and Elastic round out a team building the infrastructure layer for trustworthy enterprise AI.

The Role
You'll own the infrastructure that runs our deploy anywhere platform — from Kubernetes clusters serving ML inference at scale to the CI/CD pipelines, IaC, and observability stack that keep it all reliable. This is a hands-on role: you'll write Helm charts and Terraform one day, debug a Kafka consumer lag issue the next, and ship a backend service feature the day after. You'll deploy across AWS, GCP, and on-premises (including air-gapped environments), and you'll participate in an on-call rotation supporting enterprise customers.

What You’ll Do

  • Build and maintain infrastructure-as-code (Terraform, Helm) for our AWS EKS and GCP GKE clusters, plus on-premises deployments (including Tanzu and air-gapped environments).
  • Own CI/CD pipelines (GitHub Actions, Bazel, ArgoCD) and drive GitOps adoption.
  • Deploy, scale, and optimize ML/NLP inference workloads (vLLM, PyTorch, GPU scheduling with various Kubernetes scalers).
  • Build and improve observability: Prometheus, Grafana, Datadog,, and OpenTelemetry.
  • Collaborate with Field Engineering to support PoCs and platform deployments in customer cloud VPCs and on-prem environments.
  • Contribute to backend services (Java 21, Python, gRPC) and platform features.
  • Improve system reliability, scalability, and developer experience across the engineering org.

What You’ll Bring (Required):

  • 2+ years in platform engineering, DevOps, SRE, or backend infrastructure roles.
  • Strong Kubernetes experience (deployment, debugging, scaling — not just `kubectl apply`).
  • Hands-on with infrastructure-as-code: Terraform, Helm, or Pulumi.
  • Experience with at least one major cloud provider (AWS preferred; GCP or Azure also valued).
  • Proficiency in one or more of: Go, Python, Java. Comfortable reading and contributing to backend codebases.
  • Working knowledge of CI/CD systems (GitHub Actions, Bazel, ArgoCD, or similar).
  • Solid fundamentals in Linux, networking, and distributed systems.

What Sets You Apart (Preferred)

  • Experience deploying or operating ML inference workloads (model serving, GPU scheduling, vLLM, TensorFlow Serving, or similar).
  • Familiarity with streaming/messaging systems (Kafka, Pulsar) and data stores (MariaDB/PostgreSQL, Aerospike, ClickHouse, OpenSearch).
  • Experience with GitOps workflows (ArgoCD, Flux).
  • Exposure to air-gapped or on-premises Kubernetes deployments.
  • Background in observability tooling (Prometheus, Grafana, OpenTelemetry, Datadog).
  • Experience providing technical support or working directly with enterprise customers on infrastructure issues.
  • Comfort with AI-assisted development workflows and managing AI coding agents.

Why Join Now

Vectara is at an inflection point — strong product-market fit with 100+ enterprise customers, fresh leadership with co-founder Tallat Shafaat as CEO, and a platform that's becoming the standard for trustworthy enterprise AI. You'll work alongside pioneers in neural information retrieval, contribute to open-source projects used by millions, and build infrastructure for AI that enterprises actually trust in production. The team is small enough that your work will have an outsized impact.

Compensation and Benefits: 

At Vectara, salary represents only one part of our total compensation package. Every full-time team member is also an equity owner, and while equity is not a direct cash benefit, it offers the potential for significant long-term financial gain. We take great pride in our commitment to ensuring our employees are true economic partners in the company's success.

Vectara welcomes all. We value the collective wisdom of people from different backgrounds, experiences, abilities, and perspectives. We do not discriminate on the basis of race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status.

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