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 Security Engineer, Agentic Red Team

Deepmind · Mountain View, California, US; New York City, New York, US; Zurich, Switzerland

Snapshot

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

About Us

The Agentic Red Team is a specialized, high-velocity unit within Google DeepMind Security. Our mission is to close the "Agentic Launch Gap"—the critical window where novel AI capabilities outpace traditional security reviews. Unlike traditional red teams that hand off reports and move on, we operate with extreme agility, embedding directly with product teams as both a consulting partner and an exploitation arm. We act as a "special forces" unit capable of jumping into high-priority launches, relying on Google Core for foundational system-level protections so we can focus exclusively on model and agent-layer risks.

The Role

As a Senior Security Engineer on the Agentic Red Team, you will be the primary technical executor of our adversarial engagements. You will work "in the room" with product builders, identifying architectural flaws during the design phase long before formal reviews begin.

Your core focus will be to perform complex, multi-turn attacks on production-level AI models, specifically targeting agentic behaviors like tool usage and reasoning chains. You will not only find vulnerabilities but also help close the loop by contributing to "Auto Red Teaming" frameworks and defensive strategies, ensuring that your findings are codified into reusable guardrails for all Google agent developers.

Key responsibilities:

  • Execute Agile Red Teaming: Conduct rapid, high-impact security assessments on agentic services, focusing on vulnerabilities unique to GenAI such as prompt injection, tool-use escalation, and autonomous lateral movement.
  • Develop Advanced Exploits: Engineer and execute complex attack sequences that exploit non-deterministic model behaviors, agentic logic errors, and data poisoning vectors.
  • Build Automated Defenses: Write code to transform manual vulnerability discoveries into automated regression testing frameworks ("Auto Red Teaming") that prevent regression in future model versions.
  • Embed with Product Teams: Partner directly with developers during the design and build phases to provide immediate feedback, effectively shortening the feedback loop between offensive findings and defensive engineering.
  • Curate Threat Intelligence: Maintain and expand a library of agent-specific attack patterns and exploit primitives to establish robust release criteria for new models.

About You

In order to set you up for success as a Software Engineer at Google DeepMind,  we look for the following skills and experience:

  • Bachelor's degree in Computer Science, Information Security, or equivalent practical experience.
  • Experience in Red Teaming, Offensive Security, or Adversarial Machine Learning.
  • Strong coding skills in Python, Go, or C++ with experience building security tools or automation.
  • Technical understanding of LLM architectures, agentic workflows (e.g., chain-of-thought reasoning), and common AI vulnerability classes.

In addition, the following would be an advantage: 

  • Hands-on experience developing exploits for GenAI models (e.g., prompt injection, adversarial examples, training data extraction).
  • Experience working in a consulting capacity with product teams or in a fast-paced "startup-like" environment.
  • Familiarity with AI safety benchmarks, evaluation frameworks, and fuzzing techniques.
  • Ability to translate complex probabilistic risks into actionable engineering fixes for developers.

The US base salary range for this full-time position is between $166,000 - $244,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

 

 

 

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