Check Your Resume Before You Apply

Most employers use software (an ATS) to read and rank resumes. See your score and fix it. Free, no signup to check.

Blackforestlabs
Forward Deployed Machine Learning Engineer
San Francisco (USA)

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How it works

Key Takeaways

  • Automated hiring systems can screen or route resumes before human review; ResumeGeni treats ATS scoring as parser-readiness triage, not a hiring prediction (Harvard Business School & Accenture).
  • The most common ATS-readiness problems are missing keywords, incompatible formatting, incomplete fields, and incorrect file types
  • ResumeGeni scores parseability, structure, contact fields, content completeness, skills, and keyword signals, then explains the evidence behind the score

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 use that data in search, review, and matching workflows. Parsing gaps can make a qualified candidate harder to evaluate.

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 signalsJob-specific terms, skills, certificationsKeyword overlap can affect recruiter search visibility and resume-review workflows
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 extraction means important fields may need manual review

What ResumeGeni Checks Before Keyword Matching

Keyword matching only helps after the resume can be read cleanly. ResumeGeni starts with parser-readiness signals before it evaluates wording, skills, and role fit.

  • Readable text: whether the uploaded file exposes selectable text instead of only a scanned image.
  • Standard resume structure: whether contact, summary, work experience, education, and skills sections are easy to identify.
  • Field extraction: whether names, email addresses, phone numbers, employers, titles, dates, degrees, and skills can be mapped into stable fields.
  • Format risk: whether tables, columns, text boxes, decorative icons, headers, footers, or unusual bullets could interrupt parsing.
  • Evidence quality: whether experience bullets include scope, tools, metrics, and outcomes rather than generic duty lists.
  • Keyword coverage: whether relevant tools, certifications, industry terms, and role-specific phrases appear naturally in the resume.

What Your ATS Score Means

The score is a diagnostic signal, not a hiring guarantee. A high score means ResumeGeni can extract and evaluate the resume with fewer warnings. A low score means the resume likely needs structural fixes before keyword tuning matters.

Score RangeReadBest Next Action
90-100Strong parser readiness with few visible gapsTailor keywords and achievements to the target role
75-89Generally readable, but some sections or evidence may be weakFix warnings, add measurable achievements, and tighten skills
60-74Important content may be missing, vague, or hard to mapRepair structure before rewriting bullets
Below 60Parsing or completeness issues are likely holding the resume backMove to a cleaner format and rebuild core sections first

What To Fix First

Start with problems that prevent a system or recruiter from reading the resume. Save small wording changes for after the structure is clean.

PriorityFixReason
1Use a text-based PDF, DOCX, or plain text resumeImage-only files and corrupted exports cannot be reliably parsed
2Use one column and standard headingsPredictable structure improves section and field detection
3Put contact information in the body, not only the headerSome parsers ignore header and footer regions
4Replace vague duties with quantified achievementsSpecific outcomes help both recruiter review and scoring evidence
5Mirror role language truthfullyRelevant keywords help search and review without keyword stuffing

How To Use the Score Without Overfitting

The best use of an ATS score is triage. Fix problems that make the resume hard to parse or hard to evaluate, then stop when the document is clear. Do not chase a perfect score by adding keywords you cannot defend in an interview or by turning every bullet into a list of tools.

Checker signalGood correctionCorrection to avoid
Low parse confidenceMove to a single-column layout, standard headings, and selectable text.Adding more keywords before the resume can be read cleanly.
Weak evidence bulletsRewrite duties into scope, action, tool, and measurable outcome.Inflating impact numbers or copying sample bullets that do not match your work.
Missing role termsAdd truthful tools, certifications, patient loads, stack details, or workflows from your experience.Keyword stuffing a skills section with technologies you have not used.
Thin company fitCompare the resume with the target role and company application guide before applying.Submitting the same generic version to every employer.

Methodology And Limits

ResumeGeni checks format, extraction, content completeness, and keyword signals from the uploaded resume. It does not certify that every employer ATS will parse the file the same way, and it does not predict whether a recruiter will interview you.

For the scoring rubric, privacy notes, and limitations, read the ATS Resume Checker Methodology. For the broader source map behind ResumeGeni guidance, use the research hub and dated research data dashboard. For application context, use the exact company application guide or role guide that matches the job.

What the Checker Can Diagnose

Treat the ATS resume checker as a document-readiness diagnostic, not a hiring prediction. A useful check should tell you whether the resume text can be extracted, whether the major sections are recognizable, whether contact fields are present, whether bullets contain evidence, and whether role language appears naturally enough for a reviewer to understand the match.

Diagnostic areaWhat ResumeGeni looks forBest correction
Text extractionSelectable text, readable file structure, and parser confidence.Use a text-based PDF, DOCX, or pasted text version before changing wording.
Section recognitionStandard headings for contact, summary, experience, education, skills, projects, and certifications.Rename creative headings to conventional resume sections and keep content in the document body.
Evidence qualityBullets with scope, action, tools, and measurable outcomes rather than generic duties.Rewrite the most recent role first, then work backward through older experience.
Role alignmentTruthful keywords, credentials, systems, technologies, and responsibilities that match the target role.Compare the resume with a role guide and a real posting before adding or removing terms.

Pair the Score With a Role Guide

An ATS score is the starting point. After the resume is readable, compare it with the role you are targeting so your skills, bullets, and keywords match the actual posting without keyword stuffing.

Resume pathUse this guide when the checker flagsBest next page
ClinicalMissing license, certification, patient-load, unit, EHR, or care-outcome evidenceRN resume guide
TechnicalThin stack detail, unclear shipped features, missing testing, deployment, or performance evidenceFull-stack developer resume guide or Android developer resume guide
PortfolioCase studies, client scope, shipped work, project outcomes, or collaboration signals are too vagueProduct designer resume guide or Freelancer resume guide
People operationsHRIS, compliance, hiring, retention, employee-relations, or policy examples are missingHuman resources manager resume guide

Where This Checker Fits in the Application Path

Use the checker as a diagnostic gate between drafting and applying. It is strongest when the next action is specific: fix parsing risks, rewrite vague bullets, add missing role evidence, or compare the resume against a real posting. It is weaker when treated as a hiring predictor or a substitute for role judgment.

Signal from the checkerBest next pageReason
Formatting or parsing warningsATS compatibility methodologyReview the scoring categories and limits before changing the file structure.
Weak or generic experience bulletsResume guides by job titleFind role-specific examples and replace duties with evidence, scope, and outcomes.
Missing tools, systems, or certificationsSkills guides by job titleCheck which skills belong in the resume and which should appear only when truthful.
Company-specific application concernsCompany application guidesCompare employer context, ATS signals, and open-role language before final tailoring.

Sources Used For This Checker

ResumeGeni's checker combines product analysis with public resume-writing, occupational, and structured-data references. These sources inform parser-readiness guidance; they do not certify that any employer or ATS vendor will score a resume the same way.

Frequently Asked Questions

Is ResumeGeni free?
Yes. 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 parsed into structured fields such as contact information, experience, education, and skills. The score reflects how cleanly ResumeGeni can extract those fields plus format, content, and keyword signals.
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.

Preferred ATS Checker Resource Spine

Built by ResumeGeni. Methodology, sources, and limitations are documented above. Last updated .

Forward Deployed Machine Learning Engineer

Blackforestlabs · San Francisco (USA)

About Black Forest Labs

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world.

Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.

Why This Role

You'll live at the intersection of cutting-edge research and brutal production reality. Your customers won't just want FLUX to work—they'll need it optimized for their specific hardware, fine-tuned for their unique use cases, and integrated into systems that weren't designed for diffusion models in the first place.

What You'll Work On

  • Ensures FLUX models perform optimally in customer environments—whether that's on-premise GPU clusters or BFL-hosted infrastructure—balancing the eternal tension between latency and output quality
  • Architects deep product integrations that go far beyond "here's an API endpoint"—helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet
  • Customizes our foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible
  • Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team)
  • Discovers where generative visual AI should go next by understanding what industries are struggling with problems we could solve

What We're Looking For

You understand diffusion models not just conceptually, but viscerally—you've debugged them, optimized them, served them at scale. You've been in the room when a customer's integration goes wrong and you need to diagnose whether it's a model issue, an infrastructure issue, or a fundamental misunderstanding of what the model can do.

You likely have:

  • Direct experience working with customers on generative AI deployment—the kind where you're iterating on solutions in real-time, not just following a playbook
  • Hands-on expertise with generative modeling approaches, particularly finetuning, optimizing, and serving deep learning models in production environments
  • A proven track record as an ML engineer who's shipped models that real systems depend on
  • Strong Python skills and intuitive understanding of API design (because demos and prototypes are how you communicate what's possible)
  • The ability to explain why a diffusion model is slow to an executive and how to fix it to an engineer—in the same meeting

We'd be especially excited if you:

  • Have deep knowledge of diffusion models and/or flow matching, including finetuning and distillation techniques that go beyond standard tutorials
  • Know the FLUX ecosystem intimately—ComfyUI, common training frameworks, the tools practitioners actually use
  • Have battle-tested experience optimizing inference for transformer-based models (and the scars to prove it)
  • Can architect solutions in complex enterprise environments where "just add more GPUs" isn't an option
  • Contribute to open-source projects in the diffusion model space and understand the community
  • Have deployed models on cloud platforms using state-of-the-art serving infrastructure

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

Everything we do is grounded in four values:

  • Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful.
  • Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task.
  • Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect.
  • Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos.

Base Annual Salary:

US $180,000 - $270,000 USD + Equity