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 .

Forward Deployed EngineerLondon

Isidor · United States

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Forward Deployed Engineer

London
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About Us

Isidor is building the foundation of reliable AI. We believe that the next leap in AI depends as much on understanding and measuring model behavior as it does on model size. To make AI trustworthy, we need systems that verify outputs, benchmark performance, and automate evaluation across billions of examples.

Isidor was founded by engineers and researchers who care deeply about precision, transparency, and scientific rigor. We are a small, fast-moving team working directly with frontier AI labs and enterprise to make evaluation as measurable and reproducible as training itself. 

Founded in September 2025, Isidor raised an oversubscribed $3.6m pre-seed from leading funds including Gradient Ventures, futurepresent and Seedcamp, along with angels from Google DeepMind, Perplexity, Motive, Episode 1 and more. 

 

The Role

As a Forward Deployed Engineer, you will work directly with frontier AI labs and enterprise, to design and ship the infrastructure behind their most critical data workflows. You will embed with research teams, understand their challenges, and build the pipelines, tools, and automations that move their experiments forward.

This is not a traditional backend role. You will translate ambiguous research needs into high-leverage technical systems, blending engineering with problem-solving, and seeing the impact of your work in real time. You will ship fast, iterate constantly, and help shape how the world’s most advanced models are trained and evaluated. 

 

What We’re Looking For

  • Highly-driven, self-starters with strong proficiency in Python, including data manipulation, scripting, and automation (new grads/juniors with relevant intern/professional experience encouraged)
  • Experience with data wrangling and preprocessing, cleaning raw data, and preparing it for ingestion into machine learning systems
  • Familiarity with machine learning workflows, including feature engineering, dataset curation, and evaluation pipelines
  • Advanced coursework in mathematics and/or evidence of strong applied mathematical reasoning
  • Strong research and communication skills: ability to translate ambiguous research problems into robust, scalable technical solutions

 

What You'll Do

  • Design custom data pipelines and integrations to collect, clean, and process high-quality training and evaluation data
  • Build technical infrastructure for advanced quality control workflows, including model-in-the-loop and human-in-the-loop systems
  • Develop tools to automate experiments that improve model safety, interpretability, and human alignment
  • Prepare datasets for ingestion into machine learning pipelines, ensuring consistency, accuracy, and reliability
  • Collaborate with applied AI teams and top AI labs to turn research ideas into production-ready infrastructure

 

Why Join Us

Your work will directly influence the development of the next generation of AI models, giving you the opportunity to make an outsized impact on frontier research and implementation in enterprise. 
Our momentum: 0 → 7 figures in revenue in <1 month
Backed by: Gradient Ventures, futurepresent, Seedcamp, along with angels from Google DeepMind, Perplexity, Motive, Episode 1 and more

 

Details

Employment Type: Full time, in-office

Location: London (in-person), with potential for relocation to San Francisco in H1 2026 (with visa support)

Compensation: Competitive salary + equity

 

Benefits

  • Relocation package
  • Housing bonus (if within 3 miles of the office)

 

Please reach out with any questions to [email protected]

 

 

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