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 .

Senior Machine Learning Engineer (Swedish - speaking)

Datatonic · Stockholm

Senior Machine Learning Engineer

Shape the Future of AI & Data with Us

At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud Platform. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world.

Your Mission

As a Senior Machine Learning Engineer, you'll know how to engineer beautiful code in Python and take pride in what you produce. You'll be an advocate of high-quality engineering and best-practice in production software as well as rapid prototypes.

Whilst the position is a hands-on technical role, we'd be particularly interested to find candidates with a desire to lead projects and take an active role in leading client discussions. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements.

To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You'll be comfortable with model optimisation and deployment tools and practices. Furthermore, you'll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud.

What You’ll Do

  • Translating Requirements: Interpret vague requirements and develop models to solve real-world problems.

  • Data Science: Conduct ML experiments using programming languages with machine learning libraries.

  • GenAI: Leverage generative AI to develop innovative solutions.

  • Optimisation: Optimise machine learning solutions for performance and scalability.

  • Custom Code: Implement tailored machine learning code to meet specific needs.

  • Data Engineering: Ensure efficient data flow between databases and backend systems.

  • MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.

  • ML Architecture Design: Create machine learning architectures using Google Cloud tools and services.

  • Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.

What You’ll Bring

  • Experience: Multiple years experience as a Machine Learning Engineer, preferably with a consulting background.

  • Programming Skills: Proficiency in Python as a backend language, capable of delivering production-ready code in well-tested CI/CD pipelines.

  • Cloud Expertise: Familiarity with cloud platforms such as Google Cloud, AWS, or Azure.

  • Software Engineering: Hands-on experience with foundational software engineering practices.

  • Database Proficiency: Strong knowledge of SQL for querying and managing data.

  • Scalability: Experience scaling computations using GPUs or distributed computing systems.

  • ML Integration: Familiarity with exposing machine learning components through web services or wrappers (e.g., Flask in Python).

  • Soft Skills: Strong communication and presentation skills to effectively convey technical concepts.

Bonus Points If You Have:

  • Scale-up experience.

  • Cloud certifications (Google Cloud Professional Machine Learning Engineer, AWS Solution Architect, etc.).

What’s in It for You?

We believe in empowering our team to thrive, with benefits including:

  • Holiday: 30 days plus bank holidays (obviously!)

  • Health Perks: 4500 kr per year Healthcare Allowance (paid monthly, after probation)

  • Fitness & Wellbeing: Up to 5000 kr per year Wellness Allowance (payable on receipt of invoice)

  • Hybrid Model: 1200 kr WFH Equipment Allowance (after probation)

  • Learning & Growth: Access to platforms like Udemy to fuel your curiosity.

  • Pension: Private Salary Sacrifice Pension available

Why Datatonic?

Join us to work alongside AI enthusiasts and data experts who are shaping tomorrow. At Datatonic, innovation isn’t just encouraged - it’s embedded in everything we do. If you’re ready to inspire change and deliver value at the forefront of data and AI, we’d love to hear from you!

Are you ready to make an impact?

Apply now and take your career to the next level.