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 Python Engineer, ML

Lawhive · London

🎉 We've just raised a $60m Series B at a $685m valuation - read about it here! 🎉

About Lawhive

Our mission is to make the law accessible to everyone.

The legal industry is built on technology and processes that haven’t been updated in hundreds of years - that's why we've reinvented the entire model from the ground up with our own bespoke AI operating system at the core.

Lawhive is a regulated law firm with an AI-native platform built to amplify expertise and revolutionise the way people practice law, leading to exceptional outcomes for clients and lawyers.

Lawhive Labs is how we bring this vision to life. It's our frontier lab that combines top engineering, design, AI and legal talent from around the world, joining forces to build the future of law.

We’re backed by top-tier investors, including Google Ventures, Balderton Capital and TQ Ventures, and in early 2026, we secured $60M Series B funding round to facilitate international expansion and to grow our team.

We’re headquartered in London and in 2025 successfully launched in the US…and we’re just getting started.

Engineering at Lawhive

We are a team of 40 engineers and researchers, responsible for architecting, building and running Lawhive’s platform, relied on by hundreds of legal professionals every day in the UK, US, and beyond.

There are lots of problems we have yet to solve, and plenty we’re not aware of yet! In the next 12 months, we’re focused on:

  • Pushing the envelope on the user experience of lawyers’ AI interactions. We’re not satisfied with “chat” as a default and are inventing a new paradigm for human/machine interaction

  • Building modularity and country independence into the heart of our platform. We’ve recently expanded to the US and are building the world’s first global consumer law firm

Full stack AI-native legal services: we’re not only building software, we’re an AI-native law firm

The Role


We’re looking for a Senior Python Engineer to join our AI Engineering & Infrastructure team to bring our latest AI-driven features and services into production. Deploying them at scale, improving infrastructure, and ensuring robustness in production. You’ll work closely with AI researchers, software engineers, and product teams to bridge the gap between cutting-edge AI research and real-world applications.

Responsibilities

  • Developing production-ready APIs and services that expose AI functionality to internal and external applications.

  • Improving reliability & monitoring for AI-driven applications in production.

  • Scaling AI systems to handle real-world legal use cases (e.g., legal document analysis, case predictions, automated legal advice).

  • Collaborating with AI engineers to ensure smooth deployment of AI workflows and models into production.

  • Working with event-driven architectures and async workflows to process large-scale AI workloads efficiently.

  • Ensuring security & compliance in AI-driven legal services.

Requirements

  • Strong Python experience in building scalable backend systems.

  • Familiarity with API design & distributed systems architecture.

  • Experience working with event-driven architectures (e.g. Kafka, Pub/Sub, AWS Step Functions, etc.).

  • Experience working with cloud platforms (AWS, GCP etc).

  • Understanding of best practices in observability, monitoring, and debugging.

Nice to Have

  • LLM Observability & Evaluation – familiarity with tools such as Langfuse for monitoring model generations, managing prompts, and measuring quality in production.

  • Comfortable optimising performance & scaling distributed AI workloads and ML Ops experience.

  • Full-stack Typescript experience.

  • Hands-on work with vector databases, hybrid retrieval methods, and evaluation of retrieval quality.

  • Agentic & Orchestrated Systems – exposure to multi-step reasoning, agent frameworks, or orchestration tools (e.g. LangChain, AutoGen, Inngest, Temporal) where LLMs call tools, plan tasks, or coordinate workflows.

  • Experience working in collaboration with researchers so that new models, pipelines, or research outputs can be integrated and evolved iteratively.

  • Prior Experience in Legal Tech - understanding of the legal industry and experience working with legal technology solutions.

Interview process

  • Introductory call with our Talent team

  • 1:1 with your hiring manager

  • Live technical assessment - systems design and pairing with two of our engineers

  • Values interview with our CTO/Co-founder

  • We offer!

UK Benefits:

💰 Meaningful early-stage equity at one of Europe’s fastest growing startups

✈️ 33 days’ annual leave (25 + bank holidays) plus your birthday off

💰 Pension contribution via Nest

💷 20% off legal fees through Lawhive

💻 Top-spec equipment - MacBook/Windows

⛳️ Regular team building activities and socials!

Diversity at Lawhive

At Lawhive, we know that diversity of thought is critical to delivering outlier outcomes. As such, we’re always working hard to ensure we build a diverse, inclusive team, and as we scale, we’ll only ever increase the focus we apply to this.