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 .

AI/Machine Learning Engineer

Trovohealth · New York

About us:

Trovo Health is building the AI-powered care team platform for infinitely scalable clinical capacity. We radically increase access and improve quality of care by combining AI agents with clinical experts to take on high-impact clinical operations and care management activities for healthcare organizations.

We’re growing rapidly and are backed by Oak HC/FT, investors in leading healthcare and technology companies such as Ambience Healthcare, Devoted Health, VillageMD, CareBridge, Main Street Health, Maven Clinic, and more.

About the Role:

We are looking for a highly capable AI / ML Engineer to help build and scale Trovo Health’s AI Care Team platform. This is a broad-scope, hands-on engineering role for a technical builder with strong ML fundamentals, production experience, and a product-oriented mindset. As an early member of the engineering team, you will play a critical role in designing, training, deploying, and operating the ML systems that power Trovo’s AI coordinator.

Responsibilities:

  • Build and own AI systems end-to-end: Ingest, structure, and analyze large volumes of unstructured healthcare data, and design production-grade data and ML pipelines for both training and inference.
  • Develop NLP and LLM-powered features: Design, evaluate, and deploy models using modern NLP frameworks and LLM APIs, with a strong focus on real-world performance and reliability.
  • Operate at production scale: Architect and maintain cloud-based ML workflows in AWS, including containerized services and orchestration for data processing, training, and inference.
  • Continuously improve model quality: Monitor, test, and iterate on model accuracy, robustness, privacy, and safety within live clinical workflows.
  • Contribute across the product: Collaborate across the stack—from APIs and backend systems to product UX and workflow design—to deliver cohesive, AI-driven features.

Responsibilities:

  • Build and own AI systems end-to-end: Ingest, structure, and analyze large volumes of unstructured healthcare data, and design production-grade data and ML pipelines for both training and inference.
  • Develop NLP and LLM-powered features: Design, evaluate, and deploy models using modern NLP frameworks and LLM APIs, with a strong focus on real-world performance and reliability.
  • Operate at production scale: Architect and maintain cloud-based ML workflows in AWS, including containerized services and orchestration for data processing, training, and inference.
  • Continuously improve model quality: Monitor, test, and iterate on model accuracy, robustness, privacy, and safety within live clinical workflows.
  • Contribute across the product: Collaborate across the stack—from APIs and backend systems to product UX and workflow design—to deliver cohesive, AI-driven features.

We expect you to have:

  • ML and data depth: 3+ years of experience ingesting, structuring, and analyzing diverse data sources, with strong proficiency in Python, SQL, and data tooling (e.g., pandas or equivalent).
  • Production AI experience: Hands-on experience building and operating NLP and/or LLM-powered systems in production, including frameworks such as spaCy, LangChain, and extensive LLM API usage.
  • Pipeline expertise: Significant experience designing and maintaining data and ML pipelines in production environments.
  • Cloud and infrastructure fluency: Experience working in AWS environments, including containerized workloads and orchestration for training and inference.
  • Healthcare familiarity: Experience working with healthcare data and an understanding of the constraints of regulated environments, or strong motivation to develop this expertise.
  • Cross-functional communication: Ability to collaborate effectively with engineers, product leaders, and clinical stakeholders.
  • Ownership mentality: Demonstrated success operating in early-stage or high-growth environments with broad technical responsibility.
  • Full-stack capabilities: Comfort contributing beyond core ML work, including APIs, system design, or frontend-adjacent development when needed.
  • NYC-based: You are based in New York and excited to be in-office ~3 days per week.

Target compensation for this role is $200-$250k, plus equity and a generous benefits package.

Trovo Health is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.