LinkedIn Job Scraper

Effortlessly extract high-value job market data with our powerful LinkedIn Job Scraper. This tool automatically captures detailed listings—including job titles, company names, locations, salary insights, and posting dates—from any LinkedIn search result.

To ensure uninterrupted access, this workflow features a unique Human Handling step. This intelligent pause allows you to manually bypass LinkedIn’s security checks (like 2FA codes sent to your Gmail) before the automation takes over, ensuring a 100% login success rate without getting blocked.

Key Features

• 🛡️ Human-in-the-Loop Verification: Includes a smart "Human Handling" step that pauses the workflow during login. If LinkedIn requests a verification code, you can manually retrieve it from your Gmail, enter it into the code bar, and resume the automation instantly.
• 🎯 Customizable Search: Target specific opportunities by entering any job title (e.g., "Machine Learning Engineer") and location (e.g., "Silicon Valley").
• 📊 Flexible Data Limits: Control your extraction volume by setting a Data_Limit (e.g., 10, 50, or unlimited).
• ⚡ Smart Pagination & Scrolling: Automatically navigates through multiple search pages and handles infinite scrolling to load every available listing.
• 📝 Comprehensive Extraction: Captures essential data points: Job Title, Company, Location, Salary, and Posted Date.
• 🚫 Anti-Block Technology: Combines human handling for login with built-in wait times and smart element detection to prevent account flags.
• ☁️ Cloud & Automation Ready: Native integration with Make.com and n8n allows you to schedule runs and auto-save data to Google Sheets.

Turn LinkedIn job boards into structured data instantly. Our powerful LinkedIn Job Scraper automates the extraction of high-value market data, capturing critical details like job titles, company names, locations, salaries, and posting dates from any search result.

Designed for seamless integration with Make.com and n8n, this tool transforms manual research into an automated pipeline. Whether for market monitoring, recruitment intelligence, or lead generation, you get smart pagination, flexible filtering, and analysis-ready exports—no coding required.

Unlock deep insights by pulling publicly available data from LinkedIn. BrowserAct extracts:

• Job Titles
• Company Names
• Job Locations
• Salary Information (where available)
• Posting Date & Time

Get started instantly with our pre-built "LinkedIn Job Scraper" template.

1. Register: Create your free BrowserAct account.
2. Configure: Enter your target parameters (Search Keyword, Location, Data Limit).
3. Execute: Click "Start" to run the workflow.
4. ⚠️ Human Verification Step:
5. • The workflow will launch the browser and attempt to log in.
   • If LinkedIn asks for a code: The workflow will pause. Check your Gmail for the verification code.
   • Enter Code: Type the code into the BrowserAct "Human Interaction" input bar (or directly in the browser window if open).
   • Resume: Click the resume button. The bot will now automatically scrape all data based on your instructions.
5. Download: Export your structured dataset immediately upon completion

Scraping automates the collection of large-scale data, empowering you to make informed decisions for:

• 📈 Market Research: Track hiring trends and salary benchmarks across industries.
• 🕵️ Competitive Intelligence: Monitor competitor growth by analyzing their hiring volume and locations.
• 🤝 Recruitment Strategy: Build targeted candidate pipelines and understand compensation standards.
• 💼 Lead Generation: Identify companies that are actively hiring as prime targets for B2B services.

Build Your Workflow: Step-by-Step Guide

The BrowserAct LinkedIn Job Scraper is automation-ready and requires no coding.

Step 1: Parameter & Credential Setup Define exactly what data you need by configuring the input parameters.

• Search_Keyword: Define the role you are targeting.
• • Default Example: "Machine Learning Engineer"
• Location: Define the geographic area.
• • Default Example: "Silicon Valley"
• Data_Limit: Set the maximum number of listings to collect.
• • Default Example: 40 (Adjust higher for bulk extraction)

Step 2: The Human Handling Node This is the critical security step. We insert a Human Interaction Node immediately after the "Login" action but before the "Scrape" action.

• Action: Workflow pauses.
• Instruction: "Please check your Gmail for the LinkedIn verification code and enter it into the code bar."
• Result: Once you enter the code, BrowserAct authenticates successfully and proceeds to scrape data automatically.

Input Configuration Example:

JSON

{
  "search_keyword": "Machine Learning Engineer",
  "location": "Silicon Valley",
  "data_limit": 40
}