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.

Anima
Lead Software Engineer
Remote

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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 .

Lead Software Engineer

Anima · Remote

About Anima

Hey! Shun here, I’m the CEO and co-founder of Anima. Our mission is to deliver precision medicine to everyone in the world in under 24 hours.

My entire life, I’ve been pulling on a thread that’s affected all of us in some way. Millions die every year because their medical problems aren’t treated quickly enough. Hundreds of millions suffer pain, worry and discomfort needlessly because of long waiting times. I trained as a doctor in the NHS and quit out of frustration at seeing countless cases of people dying because they got misdiagnosed or didn’t get the right care plan.

Seeing the problem space at all abstraction levels, including as a doctor and at the HM Treasury, convinced me that the only way to fix healthcare was to build a ‘Care Enablement’ platform that can automate and abstract away work at the clinic, and effectively 10x'ing the capacity of doctors. Doing so would also be the path to a superhuman personalised medicine agent that could go well beyond humans, crunching tens of thousands of low level features at genome and transcriptome level.

At Anima, you’ll help us extend the 3 existing product lines we have, that millions of patients use, and build out new ones at the very cutting edge of healthcare reinforcement learning and agentic AI. Your work will save countless lives.

Build With Us

Today, there’s a lot of hype around ‘verticalized AI’ but when our journey started in 2021, people outside of the ML community barely knew or cared about active reinforcement learning. ChatGPT didn’t exist. I remember getting frustrated and thinking… ‘don’t you get how big of a deal this is?!’

Since Day 1 (as written in our YC application), we’ve been building towards the holy grail of personalised medicine and deep phenotyping, powered by our proprietary active learning architecture. As I said earlier, I actually submitted a 2021 patent that was prescient for the sparse MoE and active learning loop that is commonplace today with LLMs. We’ve been building and building towards this secret plan this whole time, and now have one of the biggest, highest quality labelled datasets in the world.

When everyone is technical and make great decisions, it’s much easier to stay on the same page & execute rapidly. This means we have a super short latency from ideation to real usage.

Here are 2 concrete examples with specs and timelines (we’d be delighted to demo any of these to you):

  • Alex, a clinical engineer, built ‘Slack for medical teams’, supporting 1000s of channels per organisation and a real time single source of truth for patient data, in 4 weeks, picking up a large set of new technologies on the way (ground up built from low-level services like Appsync, GraphQL, not Twilio).

  • Dennis built a lightning fast cloud document library for clinics in a few days, with <100ms traversals through preloading. Recently, when faced with an ancient legacy API that took 30s to return an array of hits, he hacked together an async indexing service with caching that reduced latency to <2s without harmful race conditions. He built and deployed to prod this in under 24 hours.

If high growth delta and joining an elite scrappy crew is your priority, you’re going to love it here.

Does this sound like you?

  • Hungry and wants their shot to change the world - a force of nature when empowered with the tools, resources and development to do it. Sees joining Anima as potentially their shot to do this, and takes duty to crew and mission extremely seriously.

  • Obsessively concerned with UX, and optimises for this when building features rather than arbitrary technical goals.

  • Bored and frustrated at big companies; feel held back by red tape, bureaucracy and poor decisions.

  • Keen to understand the big picture & entire context of the company and vertical; impatient for growth towards a senior executive role.

  • Expert competency in TS, plus being reasonably tech + language agnostic. Comfortable with key frameworks/libraries like Angular, Node and React. Able to work full stack in JS/TS. Values pragmatism and open discussion from first principles rather than dogma.

  • Seeks to maximise not only self productivity, but combined team productivity, communicating the right things at the right time through the right channels (verbal/Slack/Notion).

  • Disciplined towards best practice version control, CI/CD and code extensibility. Values ‘interface safety’ through dimensionality reduction at interfaces.

  • Exceptional at ‘breadth-first search’ through Googling when tackling new challenges, and consistently mindful of local maxima.

  • Intellectually curious with a growth mindset - able to tackle entirely novel challenges that lack prior precedent through applying strong CS fundamentals and first principles thinking, creatively using the right data structures & algorithms to solve problems 90/10.

  • Familiarity with AWS (e.g. APIG, SQS, DynamoDB, Lambda, Cognito, Amplify, CloudFormation) and/or hungry to learn.

We don’t enforce any particular experience level, but you’ll need to demonstrate most of the above through past projects and/or our assessment process.

Our current stack & what to expect from the role

We are tech agonistic, and collectively choose the best tools for the job. We’re constantly looking to maximise our productivity and minimise what we call “discounted dev time cost” for shipping features.

We have 2 separate fully functional web apps in prod: one for clinical users and one for patients. Our stack is currently entirely in JS/TS: Angular + Capacitor + Electron, React (internal tools), Amplitude (analytics), a fully serverless backend in AWS (Cognito, Appsync GraphQL, Lambda, DynamoDB). We have good functional & unit test coverage and CI/CD.

Our stack is in a great place already: highly scalable, cost effective, good test coverage, easily maintained, secure and performant with minimal to zero ops. The product and codebase are stable and loved by our users. We write, test, deploy & ship new features rapidly.

We’re looking to add talented engineers who are hungry and understand the urgency and importance of what we’re doing for society.

First month - some examples of what to expect:

  • Help add further key third party API integrations, including with legacy EMR systems and national APIs like e.g. the electronic prescribing service, allowing Anima to directly issue prescriptions.

  • Iterate on a proprietary graph traversal algorithm to improve patient care and clinical value, and increasingly move away from explicit curation to implicit curation by NNs.

  • Ship important features that will directly increase delta lives saved in your first 2 weeks.

  • Join customer calls to develop a deep understanding of their fundamental motivations and needs/pain points.

Next 6 months - some examples of what to expect:

  • Help architect and deploy a scalable & cost effective ETL data pipeline with version control, outputting clean data ready for tokenisation.

  • Help deploy our active deep learning training & validation architecture to prod, so that we can correctly eat up our ‘1.0’ systems at the right time

  • Build cutting edge products like global context aware chat with semantic search, care orchestration and LLM-enabled cloud telephony e2e

  • Hire/scale the team, while implementing the right processes at the right times to maximise discounted team productivity and minimise discounted dev time cost for shipping.

6+ months - some examples of what to expect:

  • Potential to transition to a more managerial/executive role. Lead an autonomous lance of elite engineers to fix healthcare and save lives.

  • Work with the ML/data team to creatively ideate and ship features to improve ETL pipeline throughput and quality through a data-driven approach powered by analytics.