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 II

Sumologic · United States (HQ)

Senior Machine Learning Engineer II

Sumo Logic is a cloud-native SaaS data analytics platform that solves complex observability and security problems. Customers choose our product because it allows them to easily monitor, optimize, and secure their applications, systems, and infrastructures. Our microservices architecture, hosted on AWS, ingests petabytes of data daily across multiple geographic regions. Millions of queries a day analyze hundreds of petabytes of data.

What can you expect to do?

As a Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic AI systems and related AI systems that reason over massive, heterogeneous log data. You’ll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.

You’ll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.

Responsibilities

  • Design, implement, and optimize agentic AI components, including context engineering, memory management, and prompts.
  • Develop and maintain golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.
  • Prototype and evaluate novel prompting strategies and reasoning chains for model reliability and interpretability.
  • Collaborate cross-functionally with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.
  • Operate autonomously in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.
  • Ensure reliability, performance, and observability of deployed agents through rigorous testing and continuous improvement.
  • Maintain a strong bias for action—delivering incremental, well-tested improvements that directly enhance customer experience.

Required Qualifications

  • B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
  • 4-6 years of hands-on industry experience with demonstrable ownership and delivery.
  • Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
  • Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
  • Working knowledge of LLM core concepts, prompt design, and agentic design patterns.
  • Prior experience building and deploying AI agents or LLM applications in production.
  • Familiarity with modern agentic AI frameworks (e.g., LangGraph, LangChain, CrewAI).
  • Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
  • Exposure to LLM Ops - infrastructure optimization, observability, latency, and cost monitoring.
  • Strong communication skills and a passion for shaping emerging AI paradigms.

Desired Qualifications:

  • Authored research/white papers and published in conferences. 
  • Mentored junior engineers in guiding ML applications

About Us

Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.

Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.

Compensation may vary based on a variety of factors, which include (but aren’t limited to) role level, skills and competencies, qualifications, knowledge, location, and experience. In addition to base pay, certain roles are eligible to participate in our bonus or commission plans, as well as our benefits offerings and equity awards. 

Must be authorized to work in the United States at the time of hire and for the duration of employment. At this time, we are not able to offer new non-immigrant visa sponsorship for this position.

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