Check how your resume matches a job description
Upload your resume and paste a job description to see detailed skill coverage and match analysis.
Optimized for technical roles and entry-level positions (internships, new grad, early career).
Sample Analysis
Strong MatchOverall Match Score
82
Score Breakdown
Key Insights
Strong technical background. Focus on quantifying impact and adding 4 missing skills.
How It Works
Three simple steps
Upload Resume
PDF format
Add Job Description
Paste full text
Get Insights
Instant results
Key Features
What you'll get from the analysis
Skill Coverage
Required and preferred skills breakdown
Analysis Summary
Strengths and areas to improve
Match Score
Overall compatibility percentage
Technical Deep Dive
See how it works under the hood
Technical Deep Dive
See how it works under the hood
JobFit is tuned for technical job descriptions and entry-level applicants (interns, new grads, early career). Results are most meaningful for these cases; it is not designed for senior or highly experienced roles.
Beyond Keyword Matching
Traditional ATS tools rely on exact keyword matches, missing semantic relevance when wording differs. JobFit combines semantic analysis with skill extraction to provide a comprehensive, interpretable match score.
Example:
✓ Semantic matching captures this alignment even without exact keywords
Semantic Similarity Engine
Semantic Similarity Engine
How it works:
- 1.Generate embeddings for both job description and resume using OpenAI text-embedding-3-small
- 2.Compute cosine similarity between the embedding vectors
- 3.Normalize similarity score to 0-100 range for interpretability
Why embeddings?
Embeddings capture conceptual alignment by representing text as high-dimensional vectors. Similar concepts cluster together in vector space, enabling semantic matching beyond exact word overlap.
Limitation:
While powerful for conceptual matching, embeddings alone don't indicate which specific skills are missing. That's why we combine this with explicit skill extraction.
Skill Extraction & Matching
Skill Extraction & Matching
LLM-based extraction:
Uses GPT to extract required and preferred skills from job descriptions, understanding context that regex patterns miss.
- •Required skills: Must-have qualifications
- •Preferred skills: Nice-to-have additions
Why LLM instead of regex?
- • Job description phrasing varies wildly across companies
- • Skills appear in responsibilities, qualifications, or culture sections
- • LLMs understand context (e.g., "frontend-focused role" → React/TypeScript implied)
Importance weighting:
Not all skills are equally important. The system assigns weights based on:
- • Linguistic cues ("must", "required" vs "preferred", "nice-to-have")
- • Position in job description (earlier = more important)
- • Frequency and emphasis
Multi-Signal Scoring System
Multi-Signal Scoring System
Multiple interpretable scores:
Semantic Score (0-100)
Measures conceptual alignment between your experience narrative and role expectations
Skill Match Score (weighted)
Combines required skill coverage (90% weight) and preferred skill coverage (10% weight)
Final Match Score (blended)
Weighted combination of semantic similarity and skill matching for holistic evaluation
Why multiple scores?
A single opaque number doesn't explain why you're a good or poor match. Breaking down the score into interpretable components helps you understand exactly where you stand and what to improve.