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 Software Engineer (Rust)

Symbolica · San Francisco, US

About us

Symbolica was founded on a simple idea: intelligence is about structure and reasoning, not just pattern matching.

For decades, AI has been split between symbolic systems that are reliable but brittle and neural systems that are flexible but unpredictable. We’re building a bridge between the two.

We’re an AI research lab combining deep mathematics and machine learning to create systems that truly reason. Using category and type theory as a unifying framework, we develop interpretable, reliable and scalable foundations for intelligence and turn them into real products, working in a tight feedback loop where research directly shapes application.

Agentica is the latest expression of this vision: an agent framework for tool use and multi-agent orchestration through arbitrary code execution. It recently achieved 85.28% on ARC-AGI-2, setting a new public SOTA.

Founded in 2022, we’ve raised over $30M from investors including Khosla Ventures, General Catalyst, Abstract Ventures and Buckley Ventures, backing us to rethink the mathematical foundations of machine learning.

If you’ve ever been kept awake wondering what comes after deep learning, you’ll fit right in.

About The Role

We’re looking for Senior Software Engineers with knowledge of Rust to take full ownership of delivering working systems from prototypes and high-level specs — taking ideas from the research team, turning them into production-quality systems, and shipping them quickly. You’ll work closely with researchers, ML engineers, and leadership to turn ambitious ideas into real products. You'll be expected to move fast, handle ambiguity, and fill the critical execution gap between research and production.

This role is perfect for someone who thrives on shipping — who sees incomplete specs as an opportunity, not a blocker — and who wants to build systems that work reliably, at scale, and in the real world.

📍 This is an onsite role based in our SF office.

Your Focus

  • Translate research prototypes and high-level concepts into production-quality software
  • Own deliverables end-to-end: take specifications, clarify requirements, and execute to completion
  • Build systems that are correct, reliable, maintainable, and performant
  • Work closely with ML researchers and engineers to integrate research outputs into functional systems
  • Own critical engineering tasks with high standards and attention to detail — data pipelines, API integrations, testing, error handling, system integration, and more
  • Participate in code reviews, technical discussions, and architecture planning
  • Contribute to improving processes for how we build, ship, and deliver features across teams

About You

  • 5+ years of hands-on software engineering experience
  • Proven ability to execute on incomplete specs and drive projects to completion
  • Strong experience writing production-quality code (Python & Rust strongly preferred)
  • Comfortable building full-stack systems (APIs, data pipelines, microservices, internal tools)
  • High standards for code quality, maintainability, and correctness
  • Comfortable in fast-moving, ambiguous environments where priorities shift quickly
  • Bias for ownership: you see what needs to get done and take responsibility for delivering
  • Experience supporting ML research or building ML infrastructure is a plus
  • Deep understanding of LLM architecture and inference-time optimisation.
  • Strong knowledge of the agentic ecosystem (frameworks, tools, MCP servers, MAS architectures).
  • Track record of rapid prototyping in the LLM/agent space.
  • Experience orchestrating and evaluating complex multi-agent systems.

What We Offer

  • Competitive salary and early-stage equity package
  • A high-trust, execution-first culture with minimal bureaucracy
  • Direct ownership of meaningful projects with real business impact
  • A rare opportunity to sit at the interface between deep research and real-world productization

Candidates for this position must be legally authorized to work in the United States. This position is not eligible for visa sponsorship or support.

Read more about Symbolica: