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 Applied Scientist - Search

Robin Ai · New York City

About Robin

Robin is on a mission to rebuild the legal industry — starting with making contracts simple for everyone. We are a pioneer in Legal AI, built on proprietary models, licensed data, and deep partnerships with Anthropic and AWS. Since 2019, we’ve expanded our footprint to 4 continents and have been supporting many of the world’s most successful businesses, including GE, Pfizer, KPMG, and UBS.

What will you do as a Senior Applied Scientist?

You will be leading, designing and experimenting with cutting edge research on how to best solve pressing issues surfacing in the legal domain. You will apply your expertise in machine learning, data science, and behavioural modelling to drive meaningful insights and innovations.

As AI agents become the norm in this industry, so does the need for efficient legal information retrieval, semantic search capabilities, and structured knowledge representation. Your work will bridge the gap between advanced search technologies and the complex information needs of the legal domain, making legal knowledge more accessible, navigable, and actionable.

Your day-to-day responsibilities:

  • Develop and advance knowledge extraction and representation methods - we’re particularly interested in people keen on:

    • structured knowledge extraction from legal texts and images

    • knowledge graphs/ontology engineering

    • legal knowledge base construction

    • specialised embedding methods for multimodal content in the legal domain

  • Develop methods for retrieval and reasoning over legal knowledge bases and systems (e.g. hybrid search approaches combining symbolic and neural techniques, query understanding and rewriting for legal search)

  • Performing fine-tuning and reinforcement learning to teach language models how to interact with new information architectures.

  • Building “hard” eval sets to help identify failure modes of how language models work with legal data.

  • Build infrastructure for running experiments and visualising results.

  • Work with colleagues to communicate results internally and publicly.

  • Stay updated with the latest research in machine learning, AI, knowledge representation and retrieval to bring innovative solutions to the table.

  • Mentor junior researchers and contribute to building a collaborative, knowledge-sharing culture.

Ideally, you should have the following qualifications:

  • A Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent practical experience).

  • Strong expertise in machine learning algorithms, statistical methods, and optimisation techniques.

  • Have a strong track record of scientific research (in any field), and have done work on information retrieval, knowledge representation and reasoning, structured knowledge extraction, or large-scale data analytics.

  • You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results.

  • Experience (or desire to be) working in multi-disciplinary teams.

What’s in it for you

  • Salary: up to $200,000 per annum

  • Hybrid schedule: We offer a flexible working schedule. #LI-HYBRID

  • Equity package: Generous equity scheme - everyone gets to be an owner of Robin AI!

  • Annual leave: 17 days PTO, in addition to the public holidays observed in the USA.

  • Health: Medical, dental, and vision coverage.

  • 401k retirement.

  • Growth opportunities: We prioritise promotions for high performers and help you to progress your career.

What’s it like working at Robin?

Our culture and values attract people who are creative, resourceful, and share our passion for excellence. At Robin, you're encouraged to push yourself and empowered to take risks. We support each other to think big, try new ideas, and navigate uncertainty. Whether you're at our headquarters or one of our worldwide offices, you'll find a world of opportunities to grow, thrive, and make a meaningful impact. See what life is like at Robin.

Diversity, Equity and Inclusion at Robin

We are committed to building one of the most diverse technology companies in the world. As of 2024, more than 30% of our employees come from ethnic minority backgrounds, and 51% of roles are held by women. We know that transforming the legal industry requires diverse perspectives, so we're creating an environment where innovation thrives through inclusion.

Robin operates a direct hiring model and any speculative CVs shared via agencies will be treated as a gift.

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