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 .

ML Scientist

Layerhealth · Boston or NYC

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

We’re seeking outstanding hires to join our team as early members. This is an opportunity to contribute to a high-impact, collaborative, mission-driven team, and help define the next stage of growth for Layer Health. Together, we will create the AI layer that will redefine healthcare for the better.

Here’s a collection of articles about our product, mission, recent funding round, etc.

 

Job Description

We’re hiring an exceptional ML scientist. In this role, you will be responsible for pioneering innovative machine learning techniques to advance our fundamental clinical machine learning and large language model efforts.

You can expect to:

  • Design and implement state-of-the-art machine learning techniques to advance Layer Health’s research agenda (in areas such as information extraction, multimodal reasoning, and summarization).
  • Propose new agentic methods that tackle fundamental NLP and ML challenges such as modeling over multiple documents, long contexts, multiple modalities, and with limited or noisy labels  
  • Build foundation models to power the future of clinical information extraction & synthesis, from training through inference. 
  • Stay up-to-date and actively engage with cutting-edge research in NLP, generative AI, and clinical machine learning.
  • Collaborate with the broader engineering team to ship performant products that meet user needs.
  • Cultivate and foster a robust and thoughtful R&D culture that drives the company forward.

We look for:

  • Exceptional methodological research background and experience, including but not limited to:
    • A PhD in computer science/applied mathematics or equivalent research experience, specializing in natural language processing and machine learning.
    • High-impact, early-author publications at top peer-reviewed ML journals/conferences.
  • Demonstrated record of delivering real-world impact from start to finish– with the ability to design, develop, and ship innovations.
  • Strong programming skills and fluency with modern machine learning/LLM stacks (deep learning libraries e.g. PyTorch, Jax).
  • Past experience in training/inference of foundation models (billions of parameters, distributed training, familiarity with state-of-the-art techniques).
  • A strong communicator who thrives in a customer-focused, fast-paced environment.
  • An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
  • Past experience in healthcare of life sciences is a plus, but not required.
  • We are a Boston-based company, and expect employees to meet regularly in-person in Boston (employees from Boston, NYC, or east coast are welcome).

 

Expected compensation range for this role is $200,000-250,000, in addition to stock options. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Layer Health is committed to fostering an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law.

Join us and help us transform healthcare with AI.