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 Engineer

Visia · NYC (Hybrid)

Machine Learning Engineer @ Visia

New York, NY (Hybrid/In-person)

About Us

Visia is the first multimodal AI platform custom built for heavy industry. Visia’s full-stack physical intelligence platform includes robust sensing systems across imaging modes (cameras, X-rays, cargo X-rays, LiDAR), foundation vision-language models that convert raw sensor data into structured operational intelligence, and software-driven Field Engineering that drives real transformation on-site at some of the world’s largest industrial operations.

We deploy our hardware systems powered by our model flywheel across recycling facilities, steel mills, aluminum smelters, ports, and waste-to-energy plants, processing over 1 billion data points a year. When customers adopt Visia they aren’t buying a point solution – they are parting with a platform business that turns unstructured optical data into real-time, actionable intelligence for their operations.

What We're Looking For

We're looking for a Platform Engineer to own and accelerate Visia's data flywheel — the engine that makes our fine-tuned models smarter with every deployment. You'll primarily build the tooling and automation that powers our annotation, model training, and data pipelines, ensuring that every image we process makes Solstice better at understanding the physical world.

This is a high-leverage role. The systems you build directly determine how fast we can onboard new customers, new sensor modalities, and new industry verticals. You'll also pitch in on custom Field Engineering implementations when needed and contribute to internal agentic tooling that helps the team move faster.

Responsibilities

  • Data Flywheel & Model Tooling
    • Automate and improve our instance-segmentation and vision-language model annotation tooling, creating smooth, efficient workflows for our annotators and model managers
    • Build and maintain the pipelines that move data from deployed sensors → annotation → model training → production deployment
    • Develop tooling for model performance monitoring, error analysis, and continuous improvement across customer sites
    • Work closely with ML engineers to accelerate the feedback loop between deployed models and training infrastructure
  • Field Engineering Support
    • Contribute to custom Visia implementations for enterprise customers — building integrations with scale systems, ERPs, PLCs, and customer-specific reporting
    • Support Forward Deployed Engineering (FDE) engagements by building reusable tooling and templates that make each deployment faster than the last
    • Help translate customer operational needs into technical specifications and working softwareInternal
  • Tooling & Automation
    • Build agentic tooling to accelerate GTM workflows — automated sales deck generation, customer research, and outreach
    • Collaborate with the CEO on automating operational workflows
    • Contribute to Visia's overall product roadmap and help shape the future of physical intelligence in heavy industry

Who You Are

  • You have strong programming skills in one or more commonly used programming languages (we use Python, TypeScript, and Go)
  • You move fast, ship iteratively, and care about building things that actually work in production
  • You're comfortable working with at least some of the following and eager to learn the rest:
    • Python backend development (Flask, FastAPI)
    • Frontend development (React / React Native)
    • Cloud computing (GCP, AWS, or Azure)
    • CI/CD, IaC, and containerization (Docker, Terraform, GitHub Actions)
    • Databases (SQL, dbt, Postgres, ClickHouse)
  • Bonus: experience with computer vision pipelines, annotation tooling, or ML infrastructure
  • You have proof of your engineering ability through GitHub projects or work experience

What You Might Work On

  • Building automated annotation pipelines that use SAM and vision-language models to pre-label datasets, cutting model iteration cycles by 5-10x
  • Creating model performance dashboards that surface accuracy regressions across customer sites before they become customer issues
  • Integrating Visia with a customer's scale system to automate compliance reporting they currently do by hand
  • Building an internal agent that generates customized sales decks from customer research and Visia's deployment playbooks

Compensation & Benefits

  • Compensation: $140,000-$155,000/yr  + ~$30,000/yr in equity ($170,000-$185,000/yr)
  • Health, Vision, Dental + 401(k)
  • Flexible + Unlimited PTO
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