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 Support Engineer

Lancedb · HQ

About LanceDB
LanceDB is a high-performance, open-source, cloud-native database built for multimodal workflows. From vector search at multi-billion scale to real-time retrieval, feature engineering, and analytics across large-scale datasets, LanceDB powers AI data infrastructure.
We’re looking for a hands-on, technically strong Support Engineer who will be the bridge between our engineering team and enterprise users of LanceDB, helping our customers debug distributed databases built in Rust.

Your Role

  • As one of the early team members, build our support infrastructure and practices while handling customer cases:

    • Develop and maintain knowledge-base articles, runbooks, and support tooling that document common issues, best practices, deployment patterns, and performance tuning.

    • Contribute to metrics around support response-times, resolution times, customer satisfaction, and help build a scalable support organization as we grow.

    • Work proactively: identify recurring issues, escalate product bugs or UX gaps, propose improvements in the support process, and advocate for the customer in the roadmap.

  • Serve as one of the primary technical points of contact for our customers: troubleshoot issues, respond to escalations, and guide customers through full lifecycle support for large-scale deployments of LanceDB.

  • Work in close collaboration with our engineering and product teams to reproduce issues, debug root causes, propose remediation, and drive fixes or enhancements.

  • Dive deeply into distributed database internals: query execution, storage engine, indexing, sharding, replication, fail-over, and cloud orchestration (Kubernetes, serverless-style deployments).

  • Use and contribute to Rust codebases: reproduce customer environments, inspect logs, build diagnostic tools, run instrumentation, apply patches and configuration changes.

What We’re Looking For
Must-have (please do not apply unless you meet all of the criteria in this section)

  • 8+ years of professional experience in a support / operations / troubleshooting role in a distributed database or data infrastructure environment.

  • Demonstrated experience with distributed database systems, cloud-native data platforms (AWS, GCP, or Azure), and Kubernetes or serverless deployment models.

    • Strong knowledge of distributed systems concepts: sharding, replication, consensus, failure modes, resource contention, performance bottlenecks, and cloud-native orchestration (Kubernetes, containerization, autoscaling).

  • Demonstrated experience with at least one of the following: vector/feature stores, analytics engines or big data systems.

  • Very comfortable with reading logs and correlating them with source code, working with Grafana dashboards, and creating shell scripts or Python code to assist in debugging.

  • Excellent customer-facing communication skills: you’ll be working directly with high-value customers, so you must be comfortable explaining complex technical issues clearly, managing expectations, and advocating for the customer.

  • Strong sense of ownership, urgency, correct prioritization under pressure, and ability to work closely with engineering teams to drive resolution.

  • Comfortable working in a fast-moving startup environment with high autonomy and evolving responsibilities.

Nice-to-have

  • Proficiency in Rust: you should be comfortable reading, navigating, and debugging code; ideally you’ve built or debugged production-quality systems written in Rust.

  • Familiarity with storage engine internals, indexing/data layout, performance tuning, and profiling tools.

  • Contributions to open-source projects (especially Rust), or experience writing diagnostic tools, debuggers, or instrumentation.

  • Experience deploying and monitoring systems in large-scale production environments: logging/observability (e.g., Prometheus, Grafana, OpenTelemetry), alerting, SLOs/SLAs.

  • Previous experience creating new runbooks, selecting and configuring support ticketing systems, and defining incident response processes.

Why Join Us

You’ll join a world-class team of open-source builders (co-authors of pandas, and contributors to HDFS, Arrow, Iceberg, and HBase) working on cutting-edge AI infrastructure. You’ll work together on building systems that support next-generation AI workloads, while helping define how LanceDB runs and scales in production infrastructure at some of the most innovative companies of our time.

View full listing