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 .

Research Scientist, Gemini Safety

Deepmind · Zurich, Switzerland

Research

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.

Snapshot 

The Gemini Safety team is accountable for the safety and fairness behavior of GDM’s latest Gemini models. The role of the Research Scientist will be to apply and develop data and algorithmic cutting edge solutions to advance GDM’s latest user-facing models. The workstyle is fast paced, and highly collaborative. The team has a strong culture of support, dedication and collaboration.

About Us 

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.

The role

We’re looking for a versatile Research Scientist, at ease both with figuring out how to approach new research questions, and the technical implementation of research ideas.

Our team focuses on advancing the safety and fairness behaviour of state of the art AI models. We drive the development of the foundational technology adopted by numerous product areas including Gemini App, Cloud API, and Search.

Key responsibilities:

  • Post-training / instruction tuning state of the art LLMs, focusing on text-to-text, image/video/audio-to-text modalities and agentic capabilities
  • Exploring data, reasoning and algorithmic solutions to make sure Gemini Models are safe, maximally helpful, and work for everyone.
  • Improve Gemini’s adversarial robustness, with a focus on high-stakes abuse risks.
  • Design and maintain high quality evaluation protocols to assess model behavior gaps and headroom related to safety and fairness.
  • Develop and execute experimental plans to address known gaps, or construct entirely new capabilities
  • Drive innovation and enhance understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale

About you

In order to set you up for success as a Research Scientist in the Gemini Safety team,  we look for the following skills and experience:

  • PhD in Computer Science, a related field, or equivalent practical experience.
  • Significant LLM post-training experience.

In addition, the following would be an advantage: 

  • Experience in Reward modeling and Reinforcement Learning for LLMs Instruction tuning
  • Experience with Long-range Reinforcement learning
  • Experience in areas such as Safety, Fairness and Alignment
  • Track record of publications at NeurIPS, ICLR, ICML
  • Experience taking research from concept to product
  • Experience with collaborating or leading an applied research project
  • Strong experimental taste: Good judgment regarding baselines, ablations, and what is worth testing.
  • Experience with JAX