LinkedIn Applicant Scoring

Score applicants based on CVs, LinkedIn job posting, and structured scorecards.

Model Selection

Input

The URL of the LinkedIn job posting. The job description will be automatically extracted from this URL.

List of PDF URLs containing applicant CVs/resumes to be evaluated. Each CV will be analyzed and scored individually.

URL to a PDF file containing the applicant's CV or resume

https://storage.googleapis.com/tryitnow-ai-storage/5adec113-7bc5-4c0a-88d8-0c5eef0bbba1_Example-Resume.pdf

Let AI generate score cards based on LinkedIn job posting. If enabled, you don't need to manually define score cards.

Define custom scoring categories and evaluation criteria. Each score card represents a different aspect to evaluate (e.g., Technical Skills, Experience, Education, Cultural Fit). Applicants will receive a score (0-100) and detailed rationales for each score card.

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

The name or title of this scoring category (e.g., 'Technical Skills', 'Work Experience', 'Education')

Detailed evaluation criteria for this score card. Describe what aspects should be considered and how they should be evaluated (e.g., 'Evaluate programming languages, frameworks, and technical certifications relevant to the role')

Output
John Doe
View CV
Medium Match56.25
Based on 4 score cards

Technical Skills

90
/ 100
Rationales
positive

Applicant demonstrates strong proficiency in Python, FastAPI, Node.js, PostgreSQL, Redis, Docker, and Kubernetes, which are highly relevant technical skills.

positive

Extensive experience in API design (OpenAPI, JSON Schema), distributed systems, and cloud technologies (AWS, GCP certifications, MLOps certificate) aligns well with the technical requirements.

positive

Proven ability to build and maintain high-throughput backend platforms, optimize database performance, and implement observability solutions.

Experience Match

10
/ 100
Rationales
negative

The applicant's experience is primarily in backend engineering (approx. 4.5 years), which is a significant mismatch with the job's requirement for 9 years in a creative discipline (e.g., front-end development, UX design, prototyping, motion design).

negative

There is no evidence of a portfolio displaying 'Conceptual and Technology design, prototyping and code' or experience with 'vibe coding tools' (AI Studio, Cursor, Antigravity, Firebase Studio) as explicitly required by the job description.

negative

The total professional experience (approximately 4.5 years) falls substantially short of the 9 years minimum required for a Senior Creative Technologist role.

Communication

85
/ 100
Rationales
positive

The CV is well-structured, clear, and professional, making it easy to understand the applicant's background and achievements.

positive

Project descriptions and experience bullet points effectively explain complex technical work and demonstrate strong written communication skills.

positive

Publications section indicates an ability to communicate technical topics in a broader context.

AI/ML Knowledge

40
/ 100
Rationales
positive

Bachelor's degree included a focus on 'applied machine learning' and a professional certificate in 'Cloud Computing & MLOps' indicates foundational knowledge in the field.

positive

Experience with 'AI-assisted decision systems' and a 'Resume Analyzer (ATS)' project suggests practical exposure to AI/ML concepts and workflows.

negative

The applicant's AI/ML experience is more focused on backend infrastructure and MLOps rather than direct work with AI models in a creative context or using the specific 'vibe coding tools' mentioned in the job description.

negative

Lacks explicit experience with AI models, embeddings, or the specific AI/ML tools relevant to a creative technologist role focused on 'AI launches' and 'bringing technology to developers and early adopters'.

About LinkedIn Applicant Scoring

Score applicants based on CVs, LinkedIn job posting, and structured scorecards.

How It Works

1Upload

Provide your inputs in the Playground tab — upload images, enter text, or configure parameters.

2Run

Click the run button to process your inputs through AI models — no coding or setup required.

3Get Results

View and download your AI-generated results instantly, right in your browser.

Inputs

(4 fields)
LinkedIn Job URLtextRequired

The URL of the LinkedIn job posting. The job description will be automatically extracted from this URL.

Applicant CVsarrayRequired

List of PDF URLs containing applicant CVs/resumes to be evaluated. Each CV will be analyzed and scored individually.

Auto-generate Score Cardsboolean

Let AI generate score cards based on LinkedIn job posting. If enabled, you don't need to manually define score cards.

Score CardsarrayRequired

Define custom scoring categories and evaluation criteria. Each score card represents a different aspect to evaluate (e.g., Technical Skills, Experience, Education, Cultural Fit). Applicants will receive a score (0-100) and detailed rationales for each score card.

Outputs

(1 field)
Scoring Resultsarray

Comprehensive scoring results for each applicant, including individual scores per category and detailed rationales explaining the evaluation

API Access

Integrate this use case into your application via our REST API. Switch to the API tab to see the endpoint, request format, and code examples in multiple languages.