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 Fullstack Engineer

Newtonresearch · Greater Boston Area

Staff Fullstack Engineer

Company Description

Newton Research is a fast-growing software start-up founded by repeat entrepreneurs and well-funded by blue chip venture capital firms. We are building the next generation of the closed loop media lifecycle, developing AI agents that leverage the latest in LLMs and generative AI with specialized knowledge. Our products power a wide range of workflows across brands, agencies and publishers, assisting in each step of the media planning, buying and measurement lifecycle.

Role Description

We're seeking an experienced Staff Full Stack Engineer to join our team as a technical leader and key contributor to our platform architecture. You'll drive technical strategy across our React-based frontend and Python/Django backend, making critical architectural decisions that shape how we deliver AI-driven advertising workflows at scale. A core focus of this role is designing and building systems that interface directly with LLMs—architecting prompt pipelines, managing model integrations, and creating intuitive interfaces that surface AI capabilities to end users. This role requires someone who can operate with high autonomy, mentor engineering talent, and translate complex business requirements into elegant technical solutions. You'll work across teams to establish best practices, drive technical initiatives from conception through deployment, and influence the direction of our engineering organization. The ideal candidate brings deep expertise from the adTech space, hands-on experience building LLM-powered applications, and thrives in fast-paced startup environments where technical leadership and initiative are paramount.

Responsibilities

  • Drive technical architecture and strategy across full stack features using React/Ant Design and Python/Django
  • Design and implement LLM integration patterns—including prompt engineering, context management, response parsing, and error handling for production AI systems
  • Build robust backend services that orchestrate LLM calls, manage token budgets, and ensure reliable AI-powered workflows
  • Create frontend experiences that effectively present AI-generated content and enable intuitive human-AI interaction
  • Lead the design of scalable systems that handle complex advertising workflows, real-time data processing, and high-volume API integrations
  • Make critical technical decisions that impact multiple teams and establish engineering standards and best practices
  • Own end-to-end delivery of complex, ambiguous projects that span frontend, backend, and infrastructure
  • Architect intuitive interfaces for sophisticated advertising tools while building robust backend services that integrate with major ad platforms
  • Mentor and level up senior engineers through code reviews, technical guidance, and knowledge sharing
  • Collaborate with product and data science leadership to translate business vision into technical roadmaps
  • Proactively identify systemic technical debt, performance bottlenecks, and scalability challenges before they become critical
  • Represent engineering in cross-functional planning and contribute to product strategy
  • Drive technical innovation and evaluate emerging technologies for potential adoption

Qualifications

  • 7-10+ years of full stack development experience building production applications at scale
  • Hands-on experience building applications that integrate with LLMs (OpenAI, Anthropic, or similar)—including prompt design, API integration, and handling model outputs in production
  • Deep expertise in the adTech/marTech industry - proven experience with DSPs, DMPs, ad servers, programmatic advertising platforms, or related systems
  • Expert-level proficiency with React and modern JavaScript/TypeScript ecosystems
  • Strong experience with Ant Design or similar enterprise component libraries
  • Advanced backend development skills in Python and Django with demonstrated ability to architect scalable services
  • Proven track record of technical leadership - driving architecture decisions, mentoring engineers, and influencing technical direction
  • Deep understanding of distributed systems, API design, and microservices architectures
  • Strong experience with relational databases (PostgreSQL), caching strategies (Redis), and data modeling at scale
  • Demonstrated ability to operate with high autonomy - self-directed, proactive, and able to navigate ambiguity
  • Exceptional collaboration skills and a team-first mentality - ability to build consensus and elevate those around you
  • Thrives in fast-paced startup environments - adaptable, pragmatic, and focused on business impact
  • Experience with cloud platforms (AWS preferred), infrastructure as code, and modern CI/CD practices
  • Strong system design skills with a track record of building reliable, maintainable systems

Bonus Qualifications

  • Experience with advertising APIs (The Trade Desk, Google DV360, Meta, etc.)
  • Deep knowledge of programmatic advertising workflows, bidding strategies, and measurement
  • Experience building data visualization and analytics platforms
  • Experience with advanced LLM patterns such as RAG, function calling, agent architectures, or fine-tuning
  • Experience with real-time data pipelines, event-driven architectures, and high-throughput systems
  • Contributions to open source projects or technical writing/speaking

What We Offer

  • Opportunity to shape technical strategy and architecture at a high-growth AI startup
  • High-impact staff-level role with significant autonomy and influence
  • Work directly with founders and technical leadership
  • Collaborative, innovative team culture that values technical excellence
  • Competitive compensation, significant equity, and benefits
  • Cutting-edge technology stack and complex technical challenges