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 .

Robotics Machine Learning Engineer

Rerun · Remote Europe

We’re building the data stack for Physical AI. The Rerun open source viewer is already loved by some of the best teams in the world and the Rerun Cloud is live with early design partners.

Demand is strong, and we need engineers who can sit with our customers and make them successful while feeding lessons learned back into the product. Our product is still early, and the right person will be comfortable fixing a feature and preparing a data pipeline as they are running a live demo.

What success looks like in the first 6 months

Your north star is simple: help our customers love Rerun. You'll be the primary technical interface between Rerun and a set of customers, from demo to signed contract and through deep production use. We value a strong ownership mentality.

What you'll work on

  • Machine learning:

    • Data pipelines: ingest data from physical systems, curate datasets, and convert into training datasets

    • Training and experimentation: building reference projects for embodied AI use cases such as imitation learning pipelines, sim-to-real setups, and evaluation frameworks

  • Customer love:

    • Landing customers: Running demos and building engineer-to-engineer trust alongside our founders based on machine learning work

    • Launching customers: Support onboarding customers onsite and remotely. We anticipate 30% travel for this role, with less initially and potentially growing to a larger percentage over time

    • Improving product: Mapping customer needs and one off solutions to evolve our roadmap. Sometimes building features yourself based on your deep customer understanding

We'd love it if you have

  • ML engineering background, ideally with data engineering exposure with a strong understanding of pipelines, ingestion, and the messy realities of physical-world data

  • Strong Python; C++ or Rust is a plus

  • Genuine interest in robotics, spatial AI, or computer vision

  • Comfortable talking to engineers and communicating value without losing technical depth

  • Energized by ambiguity, ownership, and wearing many hats

  • Comfortable with periodic travel and ideally excited by the prospect of being deeply embedded with a customer when it matters

How we work at Rerun

  • We're a remote company headquartered in Stockholm, Sweden.

  • We meet up in person for a week roughly once a quarter

  • The team you'll join has members in European and US timezones

  • We've put together an uncommonly talented tech team, value agency and helpfulness highly, and expect everyone to take broad responsibility for what they build

  • We offer competitive cash and equity compensation, six weeks paid vacation, and whatever hardware and software you need to do your job