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.

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.

Lyft

Machine Learning Engineer, Lyft Business

New York, NY; San Francisco, CA

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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.

Layer	What It Checks	Why It Matters
Document extraction	File format, encoding, readability	Corrupted or image-only PDFs fail immediately
Layout analysis	Tables, columns, headers, footers	Multi-column layouts break field extraction
Section detection	Experience, education, skills headings	Non-standard headings cause sections to be missed
Field mapping	Name, email, phone, dates, titles	Missing contact info is a common cause of immediate rejection
Keyword signals	Job-specific terms, skills, certifications	Keyword overlap can affect recruiter search visibility and resume-review workflows
Chronology check	Date ordering, gap detection	Reverse-chronological order is expected by most ATS
Quantification	Metrics, numbers, measurable outcomes	Quantified achievements help human reviewers and some scoring models
Confidence scoring	Overall parse quality and completeness	Low-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 Range	Read	Best Next Action
90-100	Strong parser readiness with few visible gaps	Tailor keywords and achievements to the target role
75-89	Generally readable, but some sections or evidence may be weak	Fix warnings, add measurable achievements, and tighten skills
60-74	Important content may be missing, vague, or hard to map	Repair structure before rewriting bullets
Below 60	Parsing or completeness issues are likely holding the resume back	Move 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.

Priority	Fix	Reason
1	Use a text-based PDF, DOCX, or plain text resume	Image-only files and corrupted exports cannot be reliably parsed
2	Use one column and standard headings	Predictable structure improves section and field detection
3	Put contact information in the body, not only the header	Some parsers ignore header and footer regions
4	Replace vague duties with quantified achievements	Specific outcomes help both recruiter review and scoring evidence
5	Mirror role language truthfully	Relevant 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 signal	Good correction	Correction to avoid
Low parse confidence	Move to a single-column layout, standard headings, and selectable text.	Adding more keywords before the resume can be read cleanly.
Weak evidence bullets	Rewrite duties into scope, action, tool, and measurable outcome.	Inflating impact numbers or copying sample bullets that do not match your work.
Missing role terms	Add 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 fit	Compare 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 area	What ResumeGeni looks for	Best correction
Text extraction	Selectable text, readable file structure, and parser confidence.	Use a text-based PDF, DOCX, or pasted text version before changing wording.
Section recognition	Standard 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 quality	Bullets with scope, action, tools, and measurable outcomes rather than generic duties.	Rewrite the most recent role first, then work backward through older experience.
Role alignment	Truthful 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 path	Use this guide when the checker flags	Best next page
Clinical	Missing license, certification, patient-load, unit, EHR, or care-outcome evidence	RN resume guide
Technical	Thin stack detail, unclear shipped features, missing testing, deployment, or performance evidence	Full-stack developer resume guide or Android developer resume guide
Portfolio	Case studies, client scope, shipped work, project outcomes, or collaboration signals are too vague	Product designer resume guide or Freelancer resume guide
People operations	HRIS, compliance, hiring, retention, employee-relations, or policy examples are missing	Human resources manager resume guide

Role-specific next steps

Resume guides by job title — choose the closest role guide before rewriting bullets.
Skills guides by job title — check which tools, certifications, and workflows belong in the skills section.
Keyword benchmarks — use corpus-level keyword examples as context, not as a stuffing checklist.
Company application guides — compare resume preparation with employer-specific application and ATS context.

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 checker	Best next page	Reason
Formatting or parsing warnings	ATS compatibility methodology	Review the scoring categories and limits before changing the file structure.
Weak or generic experience bullets	Resume guides by job title	Find role-specific examples and replace duties with evidence, scope, and outcomes.
Missing tools, systems, or certifications	Skills guides by job title	Check which skills belong in the resume and which should appear only when truthful.
Company-specific application concerns	Company application guides	Compare 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.

Harvard Business School and Accenture: Hidden Workers: Untapped Talent — employer screening and automated-hiring context.
CareerOneStop Resume Guide formatting guidance — public resume-formatting guidance from the U.S. Department of Labor's CareerOneStop.
O*NET OnLine — occupation tasks, skills, and role terminology used to evaluate keyword coverage.
ATS Resume Checker Methodology and ResumeGeni Research Hub — ResumeGeni's public scoring categories, limits, and preferred evidence map.

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

Research and methodology

ATS Resume Checker Methodology — the scoring rubric, upload limits, and non-guarantees.
ResumeGeni Research Hub — the preferred routing page for methodology, data, and premium guide evidence.
Research Data Dashboard — the dated corpus snapshot and data-use limits.
Keyword Benchmarks — corpus-backed keyword patterns to use as context, not as a stuffing checklist.

Product workflow

Free resume builder — rebuild structure, preview the resume, and export after the score is usable.
Resume guides by job title — choose the closest role guide before rewriting bullets.
Skills guides by job title — check which tools, certifications, and workflows belong in the skills section.
Company application guides — compare resume preparation with employer-specific application context.

Premium resume guide examples

Trust and scope

Editorial standards — how ResumeGeni creates, reviews, and corrects public guidance.
Coverage — the published, planned, parked, and refused role-resource map.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML and real business impact, shipping models that directly influence revenue, rider experience, and partner trust.

Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs.

We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection, agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software Engineers to translate complex business problems into scalable ML solutions.

This role is ideal for someone who is technically versatile, energized by variety, and wants to see their work directly shape a large-scale business.

Responsibilities:

Develop and deploy ML models across multiple problem domains — including dynamic pricing, marketplace optimization, fraud detection, and anomaly/behavior detection — in production environments serving millions of rides
Build and iterate on agentic AI systems (e.g., LLM-powered analytical agents) that automate decision-making and reduce operational overhead
Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform
Partner with Data Scientists on the Algorithms and Decisions teams to take research prototypes from proof-of-concept to production at scale
Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement
Identify new opportunities where ML can create leverage across Lyft Business verticals (Healthcare, Lyft Pass, Business Travel) and pitch solutions
Contribute to team engineering standards — code quality, observability, documentation, and testing practices

Experience:

Experience with GenAI / LLM ecosystems — prompt engineering, RAG, agent frameworks (e.g., LangChain, LangGraph), or fine-tuning
Exposure to graph-based ML methods (graph neural networks, knowledge graphs, network analysis)
Experience with pricing, marketplace, or fraud-related ML problems
Familiarity with cloud ML services (AWS SageMaker, Bedrock) or internal ML platforms
Track record of identifying and scoping ML projects independently, not just executing on pre-defined specs

Benefits:

Great medical, dental, and vision insurance options with additional programs available when enrolled
Mental health benefits
Family building benefits
Child care and pet benefits
401(k) plan with company match to help save for your future
In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
Subsidized commuter benefits
Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $176,000-$211,200, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.