Turbo LinkedIn Jobs Scraper - No Login/cookies Required

The Turbo LinkedIn Jobs Scraper is a high-performance tool designed to extract public job data from LinkedIn—fast, efficiently, and at scale. Scrape linkedin data at scale! Advanced filters to extract targeted linkedin jobs data.

Unlike traditional scrapers that rely on cookies or browser automation, this scraper operates without cookies, no login required, and no browser automation. This means:

✅ 90% Faster performance
✅ Highly targeted results with filters ✅ Lower operational costs
✅ Easier to maintain and scale

With advanced scraping techniques, no-login access, and high-speed extraction, this tool is perfect for:

• 📌 Market researchers looking to gather job market insights
• 📊 Recruiters searching for job postings and candidate trends
• 🤖 Automation workflows that need real-time job data
• 📈 Competitive analysts studying job listings and hiring patterns

You can collect thousands of listings per minute, helping you save time and get real-time job market intelligence.

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⚡ Why Choose This Scraper?

✅ Lightning-fast scraping - Upto 500+ listings/minute
✅ Zero account requirements - No cookies/logins
✅ Military-grade efficiency - Optimized parallel processing
✅ Geo-targeting ready - Location-based scraping
✅ Enterprise-scale - Built for million-record datasets ✅ Highly targeted - Target location, distance, posting date and more

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📽️ Demo Video

Take a quick look at how the project works in action! This video walkthrough highlights the key features and shows how everything comes together.

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🔧 Input Configuration

This LinkedIn Jobs Scraper is built to give you precise control over how you gather job listings from LinkedIn. Here's a detailed breakdown of each input option and how to use it effectively:

1️⃣ Job Keyword (Required)

Examples: "AI Engineer", "Healthcare Manager"

Supports Boolean search: "Java AND (Developer OR Engineer)"

2️⃣ Location (Required)

Formats: City, State, Country, or ZIP

Precision geocoding: "San Francisco Bay Area" → 37.7749°N, 122.4194°W

3️⃣ Search Radius (Optional)

• 10mi (Hyper-local)
• 25mi (Metro)
• 50mi (Regional)

4️⃣ Posting Date (Time Filters)

• Fresh data: 24hr • 7d • 30d • All historical

5️⃣ Results Limit (Safety Control)

• Default: 20
• Max: Unlimited (with proxy)

6️⃣ Proxy Setup (Mandatory)

• Residential IPs recommended
• Automatic rotation

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These input settings give you complete flexibility to scrape LinkedIn Jobs safely and effectively. Make sure to test with smaller batches first to fine-tune your configuration!

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🌟 Key Features

✅ Zero Login Required

• No LinkedIn accounts needed
• No cookie management
• Completely anonymous scraping

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✅ Smart Filters Built-In

• Location-based radius targeting
• Time-based posting filters
• Keyword combination support

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✅ Bulletproof Design

• Automatic retries for failed requests
• IP rotation via proxies
• Lightweight HTML parsing

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✅ Analysis-Ready Output

• Clean CSV/JSON formats
• Standardized date formats
• Consistent field structure

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✅ Anti-Block Technology

Built with stealth and scale in mind:

• ⚡ Uses HTTPS requests, not a browser (faster and safer)
• 🔁 Auto-scrolls and paginates through listings
• 🕒 Smart delay logic to simulate human browsing patterns
• 🛡️ Avoids detection using cookie-based login and random delays

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Want to scrape smarter, not harder? This actor is all you need to unlock LinkedIn’s job listings at scale.

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📦 Output

The final output of the LinkedIn job scraper is a structured JSON object containing detailed job information. This object can be used for storage, display, or further processing. Below is an explanation of each field along with a sample structure (placeholder values used):

🔧 Fields Description

• title: Job title as listed on LinkedIn.
• company: An object with:
  • name: Name of the company.
  • url: LinkedIn URL of the company.
  • logo: URL of the company logo.
• location: An object describing the job location.
  • text: Readable location string.
  • latitude: Geographic latitude.
  • longitude: Geographic longitude.
• posted: Date details.
  • date: ISO timestamp when job was posted.
  • text: Relative posting time (e.g. “1 week ago”).
• applicants: Applicant stats.
  • count: Number of applicants.
  • text: Readable applicant string.
• employmentType: Type of employment (e.g. FULL_TIME, CONTRACT).
• seniorityLevel: Job seniority level.
• jobFunction: Area(s) of responsibility (e.g. Engineering).
• industries: Related industries.
• description: Full HTML-formatted job description.
• salary: Salary range if available.
• requirements: Object for standard requirements.
  • education: Expected education level.
• metadata:
  • jobId: Internal/external job identifier.
  • companyId: LinkedIn company ID.
  • industryIds: Array of industry type IDs.
  • validThrough: Expiry date of the listing.
• url: Direct LinkedIn job listing URL.
• application: Application methods.
  • url: Apply URL (if any).
  • directApply: Boolean for direct application support.
• benefits: Array of mentioned job benefits.
• skills: Array of required skills (if extracted).
• hiringManager: Hiring manager's name (if available).
• similarJobs: An array of similar jobs listed in the job listing.

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Example Output

{
    "title": "Software Engineer - Backend",
    "company": {
        "name": "Plaid",
        "url": "https://www.linkedin.com/company/plaid-",
        "logo": "https://media.licdn.com/dms/image/.../plaid__logo"
    },
    "location": {
        "text": "San Francisco",
        "latitude": 37.78008,
        "longitude": -122.42016
    },
    "posted": {
        "date": "2025-04-30T11:17:34.000Z",
        "text": "1 week ago"
    },
    "applicants": {
        "count": 159,
        "text": "159 applicants"
    },
    "employmentType": "FULL_TIME",
    "seniorityLevel": "Mid-Senior level",
    "jobFunction": "Engineering and Information Technology",
    "industries": "Software Development, Technology, Information and Internet, and Financial Services",
    "description": "<p>Full job description here...</p>",
    "salary": "$163,200 - $223,200",
    "requirements": {
        "education": "bachelor degree"
    },
    "metadata": {
        "jobId": "9",
        "companyId": "2684737",
        "industryIds": ["4", "6", "43"],
        "validThrough": "2025-06-24T13:31:51.000Z"
    },
    "url": "https://www.linkedin.com/jobs/view/software-engineer-backend-at-plaid-4204412244",
    "application": {
        "url": "",
        "directApply": false
    },
    "benefits": [],
    "skills": [],
    "hiringManager": "",
    "similarJobs": [
        {
            "similarJobTitle": "Software Engineer [Level/Role]",
            "similarJobUrl": "https://www.linkedin.com/jobs/view/[job-id]",
            "similarJobcompanyName": "[Company Name]",
            "similarJobcompanyUrl": "https://www.linkedin.com/company/[company-name]",
            "similarJobpostedDate": "[YYYY-MM-DD]"
        },
        {
            "similarJobTitle": "Software Engineer [Specialization]",
            "similarJobUrl": "https://www.linkedin.com/jobs/view/[job-id]",
            "similarJobcompanyName": "[Company Name]",
            "similarJobcompanyUrl": "https://www.linkedin.com/company/[company-name]",
            "similarJobpostedDate": "[YYYY-MM-DD]"
        },
        ...
    ]
}

Save time, skip the manual work, and get the LinkedIn job data you need — quickly and reliably.

🎉 Thanks for choosing Turbo LinkedIn Jobs Scraper!

We’re thrilled to have you onboard. Turbo LinkedIn Scraper is crafted to deliver clean, structured, and ready-to-use job data directly from LinkedIn — so you can focus on what matters.

Whether you're building automations, launching a job board, or just digging into job market trends, we hope this tool saves you hours and powers up your project.

Have feedback, feature requests, or need help?
📬 Let’s chat: perfectscrape@gmail.com — or open an issue anytime. We're here for you.

Thanks again,
— Turbo LinkedIn Scraper Team ✨