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) Algorithms Engineer, Autobidder

Tesla · London, England

site:1223 type:fulltime

Join the Autobidder team on our mission to accelerate the transition to sustainable energy. We develop cutting-edge software that optimizes and automates participation of grid-connected batteries and renewable assets in wholesale electricity markets.

As a Senior Algorithms Engineer, you'll lead the development of sophisticated optimization and trading algorithms that maximize asset revenue across diverse markets. You’ll drive innovation in our core algorithmic platform, own production systems end-to-end, and translate operational insights into high-impact solutions.

  • Design, implement, and maintain advanced bidding, optimization, and forecasting algorithms in Python
  • Prototype, benchmark, deploy, and monitor algorithmic features that handle uncertainty, competitor behavior, and revenue optimization
  • Develop deep expertise in electricity markets and operational strategies
  • Make thoughtful algorithmic and infrastructure decisions that balance performance, complexity, and developer experience
  • Build tools and simulations to monitor field performance, define key metrics, and drive continuous improvement
  • Collaborate closely with machine learning engineers, traders, analysts, and software developers to deliver integrated solutions 
  • Strong proficiency in Python with over 2 years of experience developing production-grade software
  • Background in numerical optimization (LP, MILP, nonlinear), with experience using solvers like Gurobi, XPRESS, or CPLEX
  • Proficiency in Python libraries like cvxpy, pyomo, pandas, numpy, and sklearn.
  • Experience building real-world optimization products and deploying code to production systems
  • Self-starter with a passion for clean energy and collaborative problem-solving

Preferred requirements:

  • Experience in electricity markets (GB, European, or others) or energy/ancillary services trading
  • Background in operations research, stochastic/optimal control, or financial risk modeling
  • Familiarity with machine learning methods (e.g., gradient-boosted trees, ARIMA, transformers, RNNs)
  • Experience with cloud infrastructure, container orchestration, and scalable compute systems