Automate Your Job Search with N8N

I know how hard it is to search for a job. I personally had to apply to over 3000 jobs to get an internship and full time job. And this is exactly why, I provide all my resources and information for free. and I hope that even 1% of this can help you in your career. At the same time, I do this all by myself and don’t have anyone to help or any marketing budget to work with. So, if you find this article helpful, consider supporting me :

1. By making a donation through buymeacoffee

2. becoming a paid member of substack

3. or subscribing to my Youtube page.

If you need interview prep + coaching , happy to help as well and you can schedule time here

Automate Your Job Search with N8N

Recently, I discovered that you could automate your job search with N8N. And so, I decided to research on this and it is indeed true. You can definitely automate your job search with N8N and you can even do it for free for a few days with its Trial version.

You can literally apply for 1000+ jobs daily in an hour, craft personalized messages , send cold DMs and basically spend time upskilling while letting AI apply for you.

So, how is it done?

Firstly, you need to create an N8N account : Link

Here you need to enter your sign up account information. Note : it did require work email for me . But i think your gmail should also work.

Resources I used :

Directory with 20+ workflows : Link

Apply to 1000+ jobs daily video : Link

Automate your job search with Linkedin and N8N : Link

Automate entire Linkedin Job Search with AI and N8N : Link

Get Job Alerts for free when job match score is greater than 75% : Link

Download the above file locally and on N8N , click on import from file and add the json file.

Step-by-Step Workflow (Detailed)

Node 1: Schedule Trigger

• Purpose: Initiates the entire workflow automatically without manual intervention.

• Configuration: Set to execute once every 24 hours, ensuring the system checks for new job postings on a daily basis.

• Benefit: Guarantees timely updates and prevents missing out on recent opportunities.

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Node 2: Scrape Last 24 Hours Job

• Function: Uses an HTTP Request node to send a query to LinkedIn’s job search endpoint.

• Parameters: The request includes:

  • Keywords: Specific job titles, technologies, or skills (e.g., "Data Scientist", "Python", "Machine Learning").

  • Locations: Targeted geographic regions (e.g., "New York", "Remote").

  • Time Filter: Restricts results to jobs posted within the last 24 hours.

• Goal: Gather the latest and most relevant job postings.

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Node 3: Extract Job Links

• Function: Employs a Python script within a Code node to:

  • Parse LinkedIn’s HTML response.

  • Identify and extract unique job posting URLs.

• Tools: Uses BeautifulSoup or similar HTML parsing libraries.

• Outcome: Generates a clean list of valid job links for processing in later steps.

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Node 4: Split Out

• Purpose: Breaks down the consolidated list of job links into individual link items.

• Reason: Enables separate processing for each job posting, making it possible to fetch detailed info per link.

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Node 5: Loop Over Items & Wait

• Function: Iterates over each extracted job link in sequence.

• Rate Control: Implements a controlled delay (e.g., 1–3 seconds) between requests to:

  • Prevent triggering LinkedIn’s anti-scraping protections.

  • Reduce server strain.

• Benefit: Ensures a stable and reliable data retrieval process.

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Node 6: Scrape Each Job

• Function: Sends an HTTP request to each job’s dedicated page.

• Goal: Retrieve full job content, including all details not present in the search results list (e.g., complete description, benefits, and company culture notes).

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Node 7: Parse

• Function: Processes the HTML content from each job page using BeautifulSoup.

• Extracted Data Points:

  • Job Title (e.g., "Software Engineer")

  • Company Name (e.g., "Google")

  • Location (e.g., "San Francisco, CA")

  • Full Job Description (text content)

  • Direct Application Link (to apply on LinkedIn or company’s site)

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Node 8: OpenAI

• Purpose: Evaluates the relevance of each job against the candidate’s profile.

• Process:

  1. Sends the job description and candidate’s resume details as input to OpenAI’s GPT-3.5-turbo model.

  2. Requests the model to compute a relevance score (e.g., match percentage).

• Output: Receives a JSON object containing:

  • Match Score (numerical percentage, 0–100)

  • Optional reasoning or analysis text.

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Node 9: Edit Fields

• Function: Cleans and formats the JSON data returned from OpenAI for easy filtering.

• Example: Converts "match_score": "85%" to a standardized numeric value like 85.

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Node 10: Conditional Logic (If)

• Purpose: Filters job postings based on a predefined relevance threshold.

• Example Setting: Threshold = 75% match.

• Logic:

  • If match ≥ threshold: Keep the job for notification.

  • If match < threshold: Discard from results.

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Node 11: Telegram Notification

• Purpose: Notifies the user in real time about high-relevance job matches.

• Content of Message:

  • Job Title

  • Company Name

  • Location

  • Match Score

  • Direct Application Link

• Benefit: Delivers actionable job leads instantly to the user’s Telegram chat, enabling faster application submission.

GET TELEGRAM API KEYS : Link

GET CHATGPT API KEYS : Link

Credits : https://medium.com/@pechimuthu77