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 .

GTM Engineer

Lorikeet · Sydney, Australia HQ

About Us

Lorikeet is redefining what customer support looks like — and we're just getting started.

We build AI that handles complex, high-volume support for fintechs, healthtechs, marketplaces, and delivery services. Not chatbot-tier AI. The kind that actually resolves tickets, gives support teams their time back, and raises the bar for what software can do.

We're backed by over $50M USD from QED, Blackbird, and Square Peg, with backing from operators like Claire Hughes Johnson (ex-Stripe COO) and Cristina Cordova (Linear COO), plus founders of Canva, Atlassian, and Airwallex. We're the first company since Canva to be funded at early stage by all three of Australia's top VC funds — which tells you something about the calibre of people who've looked closely at what we're building and said yes.

Our customers include some of the largest telehealth, fintech, and marketplace companies in the US and Australia — many handling over a million support tickets a year. The problems are real, the scale is real, and the work matters.

If you want to build something that genuinely changes how businesses and customers interact — this is the place.

About the role

We're looking for a GTM Engineer to own the systems that generate pipeline — someone who thinks like an engineer but executes like a marketer. You'll build AI-powered marketing infrastructure, run signal-based prospecting at scale, and drive growth loops across organic, paid, and lifecycle marketing.

This isn't a traditional demand gen role. You'll use Claude Code, Clay, and modern AI tooling to build things that would have required a team of five a few years ago. You'll own experimentation velocity, not just campaign execution.

You'll report to the Head of Marketing and work cross-functionally with sales, product, and forward-deployed engineering.

What you'll do

Build AI-powered marketing infrastructure

  • Design and operate AI agents that run marketing workflows autonomously — including lead enrichment, campaign personalisation, content production, and signal monitoring

  • Build experimentation infrastructure that tests messaging, channels, and sequences faster than traditional marketing teams thought possible

  • Create self-improving systems with evals, feedback loops, and guardrails that get better without constant supervision

Run signal-based prospecting

  • Build and optimise outbound engines using Clay, Apollo, RB2B, Unify, Instantly, and similar tools

  • Surface buying signals (hiring, funding, competitor moves, contract renewals) and route them to sales with full context

  • Design enrichment workflows that prioritise accounts automatically and identify warm intro paths

Drive growth loops

  • Own lifecycle marketing: nurture sequences, re-engagement campaigns, and credit consumption alerts that drive expansion

  • Run 1:many ABM campaigns targeting industry marketing account lists

  • Build and iterate on paid campaigns (LinkedIn, Google, Reddit) with rapid A/B testing

  • Connect organic content (Steve's LinkedIn, SEO/GEO) to conversion infrastructure

Own experimentation velocity

  • Run 4–6 marketing experiments per month with clear hypotheses, metrics, and iteration cycles

  • Document what works and systematise successful experiments into repeatable playbooks

  • Track pipeline influence, reply rates, meetings booked, and cost per meeting across all channels

What we're looking for

  • 3–6 years in growth marketing, marketing ops, revenue ops, or a hybrid sales/marketing role at a B2B SaaS company

  • Genuine fluency with AI tools — building with Claude Code, Cursor, or similar, not waiting for someone else to build for you

  • Experience with the modern GTM stack: Clay, Apollo, HubSpot, Unify, or similar (or demonstrated ability to learn fast)

  • Quantitative mindset: you think in funnels, conversion rates, cohort analysis, and attribution models

  • Comfort with ambiguity: we're building the playbook, not executing an existing one

  • Consulting or product background is a plus — as is experience in growth or as an SDR/BDR

  • Strong communication skills: you'll work across sales, product, and marketing