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Smart LinkedIn job filtering with Google Gemini, CV matching, and Google Maps

AttaAtta
2308 views
2/3/2026
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What it does

The job search process is filled with manual, frustrating tasks—reading endless job descriptions only to find the seniority is wrong, the role requires a language you don't speak, or a "hybrid" job has an impossible commute.

This workflow acts as a personal AI assistant that automates the entire top of your job search funnel. It doesn't just find jobs; it reads the full description, checks the commute time from your home, filters by your specific criteria, and even compares the job requirements against your CV to calculate a match score. It's a personalized, decision-making engine that only alerts you to the opportunities that are a perfect fit.

How it works

The workflow is designed to be fully customized from a single Config node and runs in a multi-layered sequence to find and qualify job opportunities.

  1. Scrape Jobs: The workflow triggers and uses Apify to find new job postings on LinkedIn based on a list of keywords you define (e.g., "AI Workflow Engineer," "Automation Specialist").
  2. AI Triage & Smart Filtering: For each job found, a Google Gemini AI performs an initial triage, extracting key data like the job's language, work model (Remote, Hybrid, On-site), and seniority level. The workflow then applies a series of smart filters based on your personal preferences:
    • Language & Seniority: It discards any jobs that don't match your target language and experience level.
    • Commute Check: For hybrid or on-site roles, it uses the Google Maps API to calculate the commute time from your home address and filters out any that exceed your maximum desired travel time.
  3. AI Deep Analysis vs. CV: For the handful of jobs that pass the initial filters, a second, more advanced Google Gemini agent performs a deep analysis. It compares the job description against your personal CV (which you paste into the config) to generate a summary, a list of key required skills, and a final match score (e.g., 8/10).
  4. Log & Alert: The final step is action. The full analysis of every qualified job is logged in a Supabase database for your records. However, only jobs with a match score above your set threshold will trigger an immediate, detailed alert in Telegram, ensuring you only focus on the best opportunities.

Setup Instructions

This workflow is designed for easy setup, with most personal preferences controlled from a single node.

Required Credentials

  1. Apify: You will need an Apify API Token.
  2. Google Cloud: You will need credentials for a Google Cloud project with the Google AI (Gemini) and Google Maps APIs enabled.
  3. Supabase: You will need your Supabase Project URL and Service Role Key.
  4. Telegram: You will need a Telegram Bot Token and the Chat ID for the channel where you want to receive alerts.

Step-by-Step Configuration

Almost all customization is done in the Config node. Open it and set the following parameters to match your personal job search criteria:

  • MyCV: Paste the full text of your CV/resume here. This is used by the AI to compare your skills against the job requirements.

  • JobKeywords: Search keywords for jobs (e.g., "engineer", "product manager").

  • JobsToScrape: The maximum number of relevant job postings to scrape in each run.

  • HomeLocation: Your home city and country (e.g., "Breda, Netherlands"). This is used as the starting point for calculating commute times for hybrid or onsite jobs.

  • MaxCommuteMinutes: Your personal maximum one-way commute time in minutes. The workflow will filter out any jobs that require a longer travel time.

  • TargetLanguage: Your preferred language for job postings. The workflow will filter out any jobs not written in this language. You can list multiple languages, separated by a comma.

  • ExperienceLevel: The seniority level you are looking for. The AI will validate this against the job description. The value can be:

"" → (Any)
"internship" → (Internship)
"entry" → (Entry Level)
"associate" → (Associate)
"mid_senior" → (Mid-Senior Level)
"director" → (Director)
"executive" → (Executive)
  • Under10Applicants: Set to true if you only want to see jobs with fewer than 10 applicants. Set to false to see all jobs.

After setting up the Config node, configure the Supabase and Telegram nodes with your specific credentials and table/chat details.

How to Adapt the Template

This workflow is a powerful framework for any search and qualification process.

  • Change Job Source: Swap the Apify node to scrape different job boards, or use an RSS Feed Reader node to get jobs from sites that provide feeds.
  • Refine AI Logic: The prompts in the two Google Gemini nodes are the core of the engine. You can edit them to extract different data points, change the scoring criteria, or even ask the AI to evaluate a company's culture based on the tone of the job description.
  • Change the Database: Replace the Supabase node with Airtable, Google Sheets, or a traditional database node like Postgres to log your results.
  • Modify Alerts: Change the Telegram node to send alerts via Slack, Discord, or Email. You could also add a step to automatically create a draft application or add the job to a personal CRM.

Smart LinkedIn Job Filtering with Google Gemini, CV Matching, and Google Maps

This n8n workflow automates the process of finding relevant job postings on LinkedIn, filtering them based on your CV, calculating commute times, and notifying you via Telegram. It leverages the power of Google Gemini for intelligent CV matching and Google Maps for location-based filtering.

What it does

This workflow performs the following key steps:

  1. Triggers: The workflow can be executed manually or on a scheduled basis (e.g., daily, weekly) to check for new job postings.
  2. Fetches Job Postings: It sends an HTTP request to a specified API endpoint (presumably a LinkedIn job scraping service or similar) to retrieve job data.
  3. Prepares Data for AI: It transforms the raw job data into a structured format suitable for AI processing.
  4. Matches CV with Job Description (Google Gemini): For each job posting, it uses a Google Gemini Chat Model (via an AI Agent and Structured Output Parser) to compare the job description with your provided CV. This step intelligently determines the relevance and fit of the job.
  5. Filters Jobs by Relevance: An "If" node checks the output from the AI matching to filter jobs that meet a certain relevance threshold.
  6. Calculates Commute Time (Google Maps - Implied): Although not explicitly shown with a Google Maps node, the workflow's name suggests that it would calculate commute times for relevant jobs. This would likely occur after the AI matching, potentially through another HTTP Request or a dedicated Google Maps node (if available in a more complete version).
  7. Stores Relevant Jobs (Supabase - Implied): The presence of a Supabase node suggests that relevant job postings might be stored in a Supabase database for tracking or further processing.
  8. Notifies via Telegram: For each job that passes all filters (CV match, commute time), a notification is sent to a specified Telegram chat.
  9. Handles Non-Matching Jobs: Jobs that do not meet the relevance criteria are routed to a "No Operation" node, effectively discarding them.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • API for LinkedIn Job Data: An API endpoint that provides LinkedIn job postings. This could be a custom scraper, a third-party service, or a connection to LinkedIn's API (if available and configured).
  • Google Gemini API Key: Credentials for Google Gemini to power the AI matching.
  • Telegram Bot Token and Chat ID: A Telegram bot and its chat ID to send notifications.
  • Supabase Account: (Optional, but recommended if using the Supabase node) A Supabase project with a configured table to store job data.
  • Google Maps API Key: (Implied by workflow name, but not explicitly in JSON) If commute time calculation is implemented, a Google Maps API key will be required.
  • Your CV Content: The text content of your CV to be used for matching against job descriptions.

Setup/Usage

  1. Import the workflow: Download the JSON file and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Gemini Chat Model credentials with your API key.
    • Configure your Telegram credentials with your bot token and chat ID.
    • (Optional) Configure your Supabase credentials if you intend to store data.
  3. Configure HTTP Request: Update the "HTTP Request" node (ID 19) with the URL and any necessary authentication for your LinkedIn job data API.
  4. Provide CV Content: In the "Edit Fields" (Set) node (ID 38), ensure your CV content is provided as an input item, or reference it from a secure credential or external source.
  5. Configure AI Agent and Structured Output Parser: Adjust the prompts and schema within the "AI Agent" (ID 1119) and "Structured Output Parser" (ID 1179) nodes to accurately match your CV against job descriptions and extract relevant information (e.g., relevance score, reasons for match).
  6. Adjust "If" Node Logic: Modify the conditions in the "If" node (ID 20) to define your desired relevance threshold for job postings.
  7. (Optional) Implement Google Maps: If not already present, add a Google Maps node or another HTTP Request node to calculate commute times based on job locations and your preferred address.
  8. Activate the workflow:
    • For manual execution, click "Execute Workflow".
    • For scheduled execution, configure the "Schedule Trigger" (ID 839) with your desired interval.

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