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 .

Staff Software Engineer - AI Platform

CaptivateIQ · Poland

About CaptivateIQ

CaptivateIQ is the leading Sales Performance Management platform, helping enterprises design, manage, and optimize their incentive compensation programs. Backed by Sequoia, Accel, and Y Combinator, we're expanding from commission management into sales planning and enterprise performance management—all domains ripe for AI innovation.

About the Role

CaptivateIQ is building AI capabilities that will transform how enterprises manage sales performance. We're looking for a Staff Software Engineer to set the technical strategy for our Agentic SDK team, an internal developer platform that enables product teams across the company to build AI-powered features.

This is a strategic technical leadership role with multi-year, multi-team scope. You'll define the architecture and technical direction for AI at CaptivateIQ, making decisions that have no clear answer and partnering with senior Engineering, Product, and Design leadership to shape our long-term vision. You'll be responsible for technical choices that affect the entire organization, from platform design to engineering-wide quality standards.

As the technical anchor on a small, high-impact team, you'll lead by influence across organizational boundaries. You'll drive alignment between product teams consuming the Agentic SDK and the platform capabilities we build. You'll establish the bar for engineering excellence and invest deeply in coaching P4 engineers toward staff-level impact.

This is a rare greenfield opportunity to define how AI gets built at a growing enterprise software company. You'll shape not just the technology, but the culture and practices that scale with the organization.

About CaptivateIQ

CaptivateIQ is the leading Sales Performance Management platform, helping enterprises design, manage, and optimize their incentive compensation programs. Backed by Sequoia, Accel, and Y Combinator, we're expanding from commission management into sales planning and enterprise performance management—all domains ripe for AI innovation.

About the Role

CaptivateIQ is building AI capabilities that will transform how enterprises manage sales performance. We're looking for a Staff Software Engineer to set the technical strategy for our Agentic SDK team, an internal developer platform that enables product teams across the company to build AI-powered features.

This is a strategic technical leadership role with multi-year, multi-team scope. You'll define the architecture and technical direction for AI at CaptivateIQ, making decisions that have no clear answer and partnering with senior Engineering, Product, and Design leadership to shape our long-term vision. You'll be responsible for technical choices that affect the entire organization, from platform design to engineering-wide quality standards.

As the technical anchor on a small, high-impact team, you'll lead by influence across organizational boundaries. You'll drive alignment between product teams consuming the Agentic SDK and the platform capabilities we build. You'll establish the bar for engineering excellence and invest deeply in coaching P4 engineers toward staff-level impact.

This is a rare greenfield opportunity to define how AI gets built at a growing enterprise software company. You'll shape not just the technology, but the culture and practices that scale with the organization.

CaptivateIQ participates in E-Verify, web-based system that allows enrolled employers to confirm the eligibility of their employees to work in the United States

Responsibilities

  • Set the multi-year technical strategy for AI platform development, partnering with senior EPD leadership on long-term vision
  • Architect foundational AI systems that serve multiple product teams, including LLM orchestration frameworks, MCP infrastructure, and agent patterns
  • Own technical decisions with organization-wide impact where the right answer is ambiguous or contested
  • Define engineering-wide quality standards and best practices for AI development, establishing patterns that scale across teams
  • Drive technical alignment across the Agentic SDK team and product teams consuming AI capabilities
  • Invest deeply in coaching P4 engineers, helping them develop toward staff-level scope and strategic thinking
  • Represent CaptivateIQ's technical perspective in industry discussions, open-source contributions, or technical publications
  • Requirements

  • 8+ years of professional software engineering experience with demonstrated progression into staff-level technical leadership
  • Deep expertise in LLM orchestration: production experience building agent frameworks, including agent design patterns, tool integration, and workflow optimization
  • Significant experience designing MCP integrations and knowledge systems at scale, including tool server architecture, embedding pipelines, and context optimization
  • Track record of setting technical strategy that spans multiple teams and multi-year time horizons
  • Experience partnering with senior leadership (Directors, VPs) to align technical direction with business objectives
  • Demonstrated ability to make high-stakes technical decisions under extreme ambiguity
  • Strong mentorship track record, particularly in developing senior engineers toward staff-level impact
  • Demonstrated curiosity and continuous learning in the rapidly evolving AI/LLM space
  • Bonus Points

  •  Strong proficiency in Python and experience with Django or similar backend frameworks
  • Full-stack capabilities with React and TypeScript
  • Experience building and scaling internal AI/ML platforms or developer experience infrastructure
  • Familiarity with LLM orchestration frameworks (LangChain, LangGraph, or equivalent)
  • Deep expertise in advanced agent architectures: multi-agent coordination, autonomous planning, or complex tool ecosystems
  • Experience with AI evaluation, observability, and production monitoring at enterprise scale
  • Published technical writing, conference talks, or significant open-source contributions in AI/ML
  • Background building AI-powered products that shipped to enterprise customers with measurable business impact
  • Experience at a B2B SaaS company during periods of significant growth or platform expansion
  • Familiarity with sales performance management, incentive compensation, or adjacent enterprise domains
  • Notice to Prospective Candidates:

    Only emails from @captivateiq.com should be trusted.  We are aware of active recruitment scams using the CaptivateIQ name, in which individuals pose as our recruiters and post fake remote job openings and make fake job offers on the Internet. Please note, we will never do the following:
  • Attempt to correspond with a candidate using a free web-based account, such as an email address that ends in @gmail.com, @yahoo.com, @hotmail.com, etc.
  • Make an offer of employment without conducting multiple rounds of interviews face-to-face using secure video-conferencing technology.
  • Ask candidates to cash checks to buy equipment on behalf of CaptivateIQ.
  • Ask candidates to make a payment in order to be considered for a position.
  • Make early requests for candidates' personal information such as date of birth, passport details, credit card numbers, bank details and social security number, etc.
  • Please note that we’ll only ask for more sensitive personal information in connection with background checks after an offer is made.
  • CaptivateIQ works with a third-party Employer of Record to handle compliance, payroll, and as applicable benefits on our behalf
  • Participate in an on-call rotation to provide after-hours support, ensuring timely resolution of critical issues and maintaining system uptime.