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 Applied AI Engineer

Geniussports · Los Angeles, California, United States

 

 

By bringing together next-gen technology and the finest live data available, Genius Sports is enabling a new era of sports for fans worldwide, delivering experiences that are more immersive, interactive and personalized than ever before. Learn more at geniussports.com.

About the Role  

We are looking for a Senior Applied AI Engineer to build production-grade, multimodal (audio/video/text) systems that convert broadcast and radio feeds into structured, real-time signals and event candidates. You will implement and evolve “agentic” components (sensor agents, specialist agents, decision logic) that power products like Audio Intelligence, semi-automated broadcast-to-data tagging, and highlight/momentum signals. 

We will lean on your technical expertise and your pragmatic approach to problem solving; working in a team that prioritizes the principles of Agile delivery and continuous improvement. You will have a Data-driven, evidence-based mentality, comfortable with the principles of continuous experimentation and validation.  

Key Responsibilities 

  • Build and maintain multimodal agents:
    • Audio sensor agents (acoustic events, sentiment, alignment)
    • Visual sensor agents (scorebug/overlay reading, basic visual cues when applicable)
    • Specialist and decision logic components (structured event outputs, confidence, traceability) 
  • Implement streaming-friendly pipelines: chunking, normalization, time-sync, async execution, and robust retry/backoff for model/tool calls. 
  • Develop prompt-as-code with strict JSON contracts, schema validation, and deterministic post-processing to reduce brittleness.
  • Improve system robustness under noisy inputs by:
    • Designing fallback behaviors (degraded modes)
    • Adding guardrails and confidence thresholds
    • Instrumenting traces/metrics for latency + cost + accuracy
  • Partner with product, platform, and domain leads to translate sport rules/edge cases into validation logic and to integrate outputs into downstream consumers (tagging, live feeds, analytics). 
  • Contribute to the evaluation workflow by adding test cases, failure mode categories, and regression checks for prompts and model routing.
  • Stay up-to-date with emerging Gen AI technologies, tools, and best practices.
  • Mentor and support other team members in data engineering principles and practices.

  
Qualifications   

  • 5–8+ years of professional software engineering experience (backend and/or ML systems).
  • Strong proficiency in one or more of: Python, Java, Rust.
  • Hands-on experience building production services involving LLM or multimodal model integration (including Gemini, ChatGPT or Claude).
  • Comfortable with ambiguity, iterative experimentation, and evidence-based decision-making in an Agile environment.
  • Experience with streaming data platforms like Kafka, Pulsar, Flink
  • Experience with AWS Bedrock or Google Vertex AI
  • Familiarity with version control systems (e.g., Git).
  • Excellent problem-solving skills and attention to detail.
  • Ability to work independently and as part of a team.
  • Strong communication skills.

  
Preferred Qualifications   

  • Experience with audio ML / speech / acoustic event detection, or media pipelines (audio/video chunking, sync).
  • Experience with RAG or rules/config grounding for sport-specific logic (league configs, terminology, rulebooks).
  • Familiarity with evaluation practices (golden sets, precision/recall, drift monitoring) and production observability.
  • Experience operating systems where cost/latency tradeoffs matter (routing “flash vs heavy” models, caching, batching).

The salary for this role is based on an annualized range of $180,000 - $230,000 USD. This role will also be eligible to take part in Genius Sports Group's benefits plan.

We enjoy an ‘office-first’ culture and maximize opportunities to collaborate, connect and learn together. Our hybrid working models differ depending on your role and location. Occasional travel may be required.

As well as a competitive salary and range of benefits, we’re committed to supporting employee wellbeing and helping you grow your skills, experience and career. Learn more about how rewarding life at Genius can be at Reward | Genius Sports. One team, being brave, driving change 

We strive to create an inclusive working environment, where everyone feels a sense of belonging and the ability to make a difference. Learn more about our values and culture at Culture | Genius Sports.

Let us know when you apply if you need any assistance during the recruiting process due to a disability.