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 MLOps EngineerBerlin

Linkedinjobs · United States

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Senior MLOps Engineer

Berlin
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ABOUT TALON.ONE:

 

Talon.One is the most powerful incentives engine that unifies loyalty, promotions and gamification into one holistic platform. Backed by enterprise-grade security and scalability, Talon.One empowers companies to build personalized, profitable promotions and loyalty programs using any data.

Today, over 250 of the world’s most-loved brands including Adidas, Sephora and Carlsberg work with Talon.One to drive deeper engagement and lasting loyalty with their customers.

 

ABOUT THE TEAM:

We're a passionate cross-functional team of engineers, data scientists, product manager, and an engineering manager, dedicated to all things data. You'll be at the centre of our expanding Intelligence Tribe. We're enthusiastic about exploring new tools and technologies, and we thrive on brainstorming to solve challenges and apply best practices.

 

ABOUT THE ROLE:

We are seeking a Senior MLOps Engineer to join our growing Intelligence Platform team. In this role, you will be responsible for designing, developing, and maintaining robust MLOps infrastructure and workflows that support the deployment, monitoring, and scalability of our machine learning models. You’ll work closely with data scientists, engineers, and Infrastructure team to streamline the machine learning lifecycle and bring cutting-edge solutions into production.

ONCE YOU ARE HERE, YOU WILL:

  • Build and maintain MLOps pipelines to automate data preparation, model training, evaluation, deployment, and monitoring of ML models and data pipelines.
  • Design scalable infrastructure using our cloud platform and tools like Kubernetes, Docker, and Terraform.
  • Implement CI/CD for ML models, ensuring reproducibility, reliability, and version control.
  • Monitor model performance and data drift in production, and implement automated retraining workflows as needed.
  • Collaborate with data scientists and engineers to operationalize machine learning models in a secure, efficient, and scalable manner.
  • Maintain model registries, metadata tracking, and experiment management tools.
  • Advocate for and help implement best practices in MLOps across the organization.
  • Ensure compliance with security, governance, and ethical AI guidelines.

 

WHAT WE EXPECT YOU TO BRING TO THE TABLE:

  • 6+ years of experience in software engineering or DevOps, with at least 3+ years in MLOps or ML infrastructure roles.
  • Experience deploying and managing machine learning systems at scale, preferably in high-availability, real-time environments with low latency and high throughput.
  • Experience with building or maintaining scalable ML infrastructure (e.g., CI/CD for models, auto-scaling, load balancing, blue-green deployment, canary releases, shadow testing, etc.).
  • Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
  • Proficiency with Google Cloud Platform and infrastructure-as-code tools like Terraform.
  • Hands-on experience with tools like Vertex AI, MLflow, Kubeflow, Airflow, or SageMaker.
  • Experience with containerization and orchestration technologies (Docker, Kubernetes).
  • Experience implementing monitoring and alerting systems for ML pipelines and APIs.

 

WHAT'S IN IT FOR YOU:

  • A Research and Development Department of 90+ of engineers, product managers and product designers in Berlin
  • Leaders with 8+ years of experience building our promotions engine
  • €1,000 annual learning budget, full LinkedIn Learning access, and free German language courses to boost your skills
  • 30 days of annual leave, plus extra paid days for your birthday and moving day
  • Home office setup budget, a monthly home office allowance
  • Freedom to work from abroad for up to 90 days worldwide!
  • Mental health support with nilo.health and a discounted Urban Sports Club membership
  • 20% company subsidy on your pension contributions
  • Discounted BVG public transport ticket and bring along your furry friend to our dog-friendly Berlin office!
  • Lease your ideal bike through BusinessBike

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