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 .

Software Engineer - Backend

Specter · San Francisco

Company Background

Specter is creating a software-defined “control plane” for the physical world. We are starting with protecting American businesses by granting them ubiquitous perception over their physical assets.

To do so, we are creating a connected hardware-software ecosystem on top of multi-modal wireless mesh sensing technology. This allows us to drive down the cost and time of deploying sensors by 10x. Our platform will ultimately become the perception engine for a company’s physical footprint, enabling real-time perimeter visibility and autonomous operations management.

Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast-approaching world of physical AI and robotics. We are a small, fast-growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.

Role + Responsibilities

Specter is hiring an infrastructure software engineer to design, deploy, and scale distributed systems that power our mother node base stations and cloud services. This role owns the reliability, scalability, and performance of Specter’s sensing and perception platform across edge devices and cloud infrastructure.

Key responsibilities include:

  • Architecting and operating distributed, Linux-based systems across edge and cloud environments.

  • Designing scalable pipelines for streaming, storing, and processing vast amounts of video and sensor data.

  • Building containerized services (Docker/Kubernetes) to deploy perception and inference workloads at scale.

  • Developing observability systems for monitoring, logging, and alerting across a fleet of edge nodes and cloud infrastructure.

  • Optimizing queue-based video ingestion, transcoding, and upload pipelines for reliability under bandwidth-constrained environments.

  • Implementing highly-available infrastructure for real-time alerting, APIs, and customer-facing event systems.

  • Working cross-functionally with hardware, perception, and application teams to ensure infrastructure supports rapid iteration and scale.

Preferred Qualifications

  • Deep experience with databases, storage, and caching technologies (PostgreSQL, Redis, object stores like S3, and distributed file systems).

  • Expertise building and maintaining real-time streaming and event systems (WebRTC, Websockets, Kafka, or similar).

  • Strong experience designing and securing cloud infrastructure using AWS (preferred), GCP, or Azure.

  • Hands-on experience with infrastructure-as-code tools (Terraform, Ansible) and container orchestration (Kubernetes).

  • Strong knowledge of distributed systems principles — replication, consensus, partition tolerance, fault recovery.

  • Familiarity with video pipelines, transcoding, and formats is a plus.

  • Prior experience scaling edge-to-cloud systems or operating at the infra layer of robotics and IoT a bonus.