How Algorithms Read Your Resume

You’ve spent hours perfecting your resume. Every bullet point is a polished gem, every accomplishment quantified. You hit “submit” and imagine a recruiter thoughtfully poring over your experience. The reality, however, is that the first “reader” of your resume is almost certainly not human. It’s an Applicant Tracking System (ATS), and it doesn’t read your resume in the way you or I would. It parses it.

This distinction is more than just semantics; it’s the key to understanding why some perfectly good resumes get discarded before a human ever sees them. An ATS is a piece of software guided by linguistic rules—a kind of grammar-obsessed robot that breaks down your carefully crafted document into a database of searchable information. To get past this digital gatekeeper, you need to speak its language. Let’s break down the linguistic rules these algorithms use to deconstruct your resume.

Keyword Matching: The Lexical Foundation

The most basic function of an ATS is lexical analysis, a fancy term for identifying and counting words. When a recruiter posts a job, they load the description into the ATS and highlight the key skills and qualifications they’re looking for. The system then scans every submitted resume for these exact words and phrases.

This is a game of exact matches. The ATS isn’t inferring meaning; it’s checking boxes. If the job description asks for experience with “digital marketing”, the system searches for the string “digital marketing.”

• Job Description says: “Experience with Google Analytics and Search Engine Optimization (SEO).”
• Your Resume says: “Helped improve our website’s search ranking.”

A human reader understands these are the same thing. But a basic ATS might miss the connection because the specific keywords “Search Engine Optimization” or “SEO” are absent. It’s not intelligent; it’s literal.

How to Speak Its Language:

• Mirror the Job Description: Carefully read the job posting and incorporate its specific terminology into your resume. If it lists “project management”, use that exact phrase, not just “oversaw projects.”
• Use Both Acronyms and Full Phrases: To cover all bases, include both the full term and its acronym. For example, write “Customer Relationship Management (CRM)” to ensure you match whichever version the ATS is programmed to find.

Semantic Analysis: Getting Smarter with Meaning

Modern ATS platforms are evolving beyond simple keyword matching. They now employ a degree of semantic analysis, which means they try to understand the meaning behind the words and the relationships between them. This is primarily seen in how they handle job titles and related skills.

An ATS is trained on millions of data points from real resumes and job descriptions. Through this training, it learns that certain job titles are functionally similar. It understands that a “Software Engineer”, a “Software Developer”, and a “Programmer” likely have overlapping skill sets. It can also infer hierarchy—that a “Senior Content Strategist” is more experienced than a “Content Coordinator.”

This allows the system to cast a wider, more intelligent net. If a company is hiring a “Communications Manager”, the ATS won’t just look for that exact title. It will also flag candidates with titles like:

• Public Relations Manager
• Corporate Communications Lead
• Marketing Communications Specialist

The system then contextualizes the skills listed under that job. It expects to see terms like “press releases”, “media relations”, “internal communications”, and “brand messaging” associated with these roles. The presence of these related skills reinforces the match.

How to Speak Its Language:

• Use Standard Job Titles: While “Marketing Rockstar” or “Coding Ninja” might sound exciting, an ATS has no semantic framework for these. Stick to industry-standard titles.
• Provide Clarification: If your official title was unique or internal-facing, add a more standard equivalent in parentheses. For example: “Innovation Catalyst (Product Development Manager).” This gives the ATS a familiar anchor point.

The Grammar of a Resume: Structures the ATS Recognizes (and Misses)

This is where the linguistic rules get fascinating. An ATS is programmed to recognize specific grammatical patterns to extract information about your accomplishments. It doesn’t appreciate prose; it appreciates predictable structure.

The Golden Rule: Action Verb + Noun Phrase

The most easily parsable structure for a resume bullet point is a sentence that starts with a strong action verb, followed by a noun or phrase that describes the result or object of that action.

Consider this bullet point: “Developed a comprehensive social media strategy that increased user engagement by 40%.”

Here’s how the ATS likely parses it:

• Action Verb: “Developed” (Identified as a skill/action)
• Noun Phrase/Object: “a comprehensive social media strategy” (The “what” of the action)
• Context/Result: “increased user engagement by 40%” (Quantifiable outcome)

This structure allows the system to neatly categorize your experience: Skill = Development; Area = Social Media Strategy; Result = 40% increased engagement. It’s clean, structured data.

What the Parser Misses

Because the ATS relies on these predictable patterns, it can be easily confused by more complex, creative, or grammatically unconventional sentences.

• The Passive Voice: “A comprehensive social media strategy was developed by me…” The ATS struggles with passive voice. It might fail to correctly attribute the action (“developed”) to you, the subject. Always use active voice: “I developed…” or simply “Developed…”
• Figurative Language: “I knocked it out of the park with our Q4 email campaign.” A human understands this means you were highly successful. An ATS sees the words “knocked”, “park”, and “email campaign” and can’t form a logical connection. It misses the accomplishment entirely.
• Complex Formatting: The biggest parser-killer is visual formatting. The ATS reads text in a simple, linear sequence (left-to-right, top-to-bottom). This means:
  • Columns: If you put your skills in a right-hand column, the parser might read across the page, jumbling your bullet points with your skills list, creating nonsensical sentences.
  • Tables, Text Boxes, and Graphics: Many systems cannot read text embedded in tables, text boxes, headers, footers, or images. That perfect-looking skills chart you created? It might be completely invisible to the ATS.

Writing for Two Audiences

Crafting a modern resume is a linguistic balancing act. Your primary goal is to write a document that successfully passes through the ATS filter by speaking its rigid, rule-based language. This means using keywords, standard titles, simple formatting, and a predictable “Action Verb + Result” grammatical structure.

But remember, the ultimate audience is human. Once your resume is parsed and ranked highly, a recruiter will read it. Your resume must still be compelling, readable, and persuasive to them. The good news is that the principles that make a resume ATS-friendly—clear language, strong action verbs, and quantifiable results—are the same principles that make it impressive to a human reader. By understanding the linguistics of the machine, you’re not just gaming a system; you’re building a stronger, more effective resume for everyone who reads it.