LinkedIn Jobs Scraper — B2B Leads & Salaries

LinkedIn Jobs Scraper — B2B Leads & Salaries

LinkedIn Jobs Scraper extracts structured LinkedIn Jobs records, metadata, metrics, and direct links in real time. Built on a zero-browser HTTP/2 engine running in 256 MB RAM, it delivers normalized datasets with zero null fields ready for CSV, Excel, JSON, or API pipelines.

Looking for related scrapers? Check out LinkedIn Profile Scraper, LinkedIn Company, LinkedIn Posts Scraper, Glassdoor Scraper, or browse our full catalog of 118+ dedicated scrapers.

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What does LinkedIn Jobs Scraper do?

Extract thousands of LinkedIn job postings anonymously without cookies or login. Includes job titles, companies, locations, dates, applic.... Compatible Make.com, Zapier, n8n, Sheets & Webhooks. 256MB RAM & sub-second speed.

Whether you need to monitor LinkedIn Jobs at scale, enrich B2B/B2C databases, or feed structured records into analytics and AI workflows, LinkedIn Jobs Scraper handles pagination, field normalization, and schema validation automatically:

• Bulk Data Extraction: Collect LinkedIn Jobs listings, items, and detailed attributes in batches using keywords, filters, or direct URLs.
• Complete Public Metadata: Capture identifiers, pricing/engagement metrics, author/vendor details, timestamps, and canonical URLs.
• Multi-Format Export: Download results in JSON, CSV, Excel (XLSX), XML, or HTML Table directly from the Apify Console.
• Ready for Automation & AI: Connect seamlessly to Apify Integrations (Make, Zapier, n8n, Google Sheets), REST API endpoints, or the Apify MCP Server.

Beyond the Official LinkedIn Jobs API

• No API Keys or Approval Delays: Start extracting public LinkedIn Jobs data immediately without registering developer apps or waiting for quota approvals.
• No Restrictive Rate Quotas: Bypass strict per-minute API limits and expensive enterprise tiers while capturing the complete public data points displayed on LinkedIn Jobs.
• 256 MB Zero-Browser Architecture: Uses native HTTP/2 and TLS session emulation instead of heavy Chromium instances, executing in seconds at a fraction of standard compute costs.

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What data can you scrape from LinkedIn Jobs?

LinkedIn Jobs Scraper extracts the following data points from LinkedIn Jobs:

📝 Queries (queries)	📅 Location (location)	📝 Under10 Applicants (under10Applicants)
📅 Posted Date (postedDate)	🆔 Job ID (jobId)	📝 Title (title)
👤 Company (company)	🖼️ Company Logo (companyLogo)	🔗 Company URL (companyUrl)
🔗 URL (url)	💰 Salary (salary)	📝 Applicants Count (applicantsCount)

Data Category	Extracted Fields & Data Points
Identity & Core Identifiers	jobId (Job ID), title (Title), employmentType (Employment Type)
Metrics, Pricing & Engagement	salary (Salary), applicantsCount (Applicants Count)
Details, Attributes & Context	queries (Queries), under10Applicants (Under10 Applicants), company (Company), seniorityLevel (Seniority Level), jobFunction (Job Function), industries (Industries)
Direct Links, Media & Timestamps	location (Location), postedDate (Posted Date), companyLogo (Company Logo), companyUrl (Company URL), url (URL), scrapedAt (Scraped At)

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Why scrape LinkedIn Jobs? (Typical Use Cases & Guides)

You can use LinkedIn Jobs Scraper for a wide range of data and business workflows:

• B2B Sales & Job Change Triggers: Reach out to companies actively hiring specific roles (strong buying signal).
• Staffing Agency Candidate Pipeline: Match top tech talent with fresh roles having low applicant competition.
• Competitive Intelligence: Monitor competitor department expansions and technological stack migrations.
• AI Agents, RAG & LLM Knowledge Pipelines: Stream clean, structured LinkedIn Jobs JSON records into vector databases, Claude, GPT-4, or LangChain workflows.
• Automated BI Dashboards & Scheduled Alerts: Schedule daily or hourly runs and push live LinkedIn Jobs updates directly to Google Sheets, Looker Studio, Slack, or webhooks.
• Market Research & Competitor Analysis: Benchmark LinkedIn Jobs offerings, pricing strategies, and market share across regions.
• Lead Generation & CRM Enrichment: Build targeted prospect lists and sync verified records straight into HubSpot, Salesforce, or Clay.
• AI Agents & RAG Pipelines via MCP: Connect live LinkedIn Jobs data directly to Claude Desktop, Cursor, or LangChain agents.

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How to scrape LinkedIn Jobs data

LinkedIn Jobs Scraper is designed for a zero-configuration start, even if you have never scraped data before:

1. Create a free Apify account using your email (includes $5.00 in free monthly credits).
2. Open LinkedIn Jobs Scraper and click on the Input tab — all required fields are already pre-filled with a working example.
3. Enter your search terms, target URLs, or filter criteria (or keep the default pre-filled configuration for an instant 1-Click test).
4. Click the green "Start" button and wait a few seconds for the data to be extracted.
5. Download your dataset in JSON, CSV, Excel (XLSX), or HTML, or fetch it via the API tab.

For more technical details on every parameter, check out the Input Schema tab or read the Apify Platform Documentation.

🔗 How to scrape LinkedIn Jobs by Search Term or Direct URL

Scraping by Search Term / Filter 🔑	Enter keywords, categories, or filter parameters just as you would search on LinkedIn Jobs. Set the maximum number of items you want to extract and click Start.
Scraping by Direct URL 🔗	Paste one or more direct LinkedIn Jobs links (or import a list of URLs from a Google Sheet / CSV) to extract targeted item or profile details in bulk.

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⬇️ Input Configuration & Examples

To start scraping LinkedIn Jobs, fill in the visual input form in the Apify Console or pass a JSON payload via the API. Full technical specifications are available on the Input Schema tab.

Input Parameters Reference

Parameter	Type	Default / Prefill	Description
queries	array	["Software Engineer"]	Job titles or keywords to search for (e.g. 'Software Engineer', 'Data Scientist', 'Product Manager').
location	string	France	City, country, or region (e.g. 'France', 'Paris', 'United States', 'Remote').
companyFilter	string	—	Optional company name to filter job postings by (e.g. 'Google', 'Amazon', 'Doctolib').
under10Applicants	boolean	false	Filter for early bird job opportunities with fewer than 10 applicants.
postedDate	string	anyTime	Filter postings by publication date. Options: anyTime, pastMonth, pastWeek, past24Hours.
experienceLevel	string	all	Filter by candidate seniority level on LinkedIn. Options: all, internship, entry_level, associate, mid_senior, director.
workplaceType	array	["1","2","3"]	Filter by workplace arrangement.
scrapeFullDescription	boolean	false	Fetch the full text description, responsibilities, and requirements for each job.
sessionCookie	string	—	Optional 'li_at' cookie for authenticated high-throughput scraping without guest rate limits.
maxItems	integer	10	Maximum number of job postings to retrieve in total.
proxyConfiguration	object	—	Use Apify Proxy to prevent LinkedIn rate limits during high-volume scraping.

Example 1: Standard Extraction (1-Click Default)

{
  "queries": [
    "Software Engineer"
  ],
  "location": "France",
  "under10Applicants": false,
  "postedDate": "anyTime"
}

Example 2: Filtered & Batch Extraction

{
  "queries": [
    "Software Engineer"
  ],
  "location": "France",
  "under10Applicants": true,
  "postedDate": "pastMonth",
  "experienceLevel": "internship",
  "workplaceType": [
    "1",
    "2",
    "3"
  ],
  "scrapeFullDescription": true,
  "maxItems": 20
}

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⬆️ Output Example (Table View, JSON & Fields Reference)

The scraped results are stored in a structured Apify Dataset available in the Storage tab. Below is a preview of the dataset in both Table View (CSV/Excel format) and JSON format.

Table View (CSV / Excel Dataset Preview)

Queries	Location	Under10 Applicants	Posted Date
Software Engineer	France	false	anyTime
Software Engineer	France	false	anyTime
Software Engineer	France	false	anyTime

JSON Output Example

{
  "queries": [
    "Software Engineer"
  ],
  "location": "France",
  "under10Applicants": false,
  "postedDate": "anyTime"
}

Output Fields Reference

Field Name	Type	Description
queries	array	Extracted queries value.
location	string	Extracted location value.
under10Applicants	boolean	Extracted under10 applicants value.
postedDate	string	Extracted posted date value.
jobId	string	Extracted job id value.
title	string	Extracted title value.
company	string	Extracted company value.
companyLogo	string	Extracted company logo value.
companyUrl	string	Extracted company url value.
url	string	Extracted url value.
salary	string	Extracted salary value.
applicantsCount	number / string	Extracted applicants count value.
seniorityLevel	string	Extracted seniority level value.
employmentType	string	Extracted employment type value.
jobFunction	string	Extracted job function value.
industries	string	Extracted industries value.
description	string	Extracted description value.
query	string	Extracted query value.
scrapedAt	string	Extracted scraped at value.

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🔗 Scrape any data you need with dedicated scrapers

Combine LinkedIn Jobs Scraper with our other dedicated scrapers from @RBBscrap if you want to extract specialized data across platforms:

🔹 LinkedIn Profile Scraper	🔹 LinkedIn Company	🔹 LinkedIn Posts Scraper
🔹 Glassdoor Scraper	🔹 France Travail Scraper	🔹 Upwork Scraper

Scraper	Extracted Data & Primary Use Case
LinkedIn Profile Scraper	B2B Leads & Emails
LinkedIn Company	B2B Data & Tech Stack
LinkedIn Posts Scraper	Posts, Search & Comments
Glassdoor Scraper	Salaries, Reviews & Jobs
France Travail Scraper	700K+ FR Job Offers (Pôle Emploi)
Upwork Scraper	Freelance Jobs & Budgets

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⚠️ Error Items & Resilience Reference

LinkedIn Jobs Scraper is engineered to process multi-query and batch runs resiliently without crashing if a single query or URL fails:

Status / Code	Meaning & Automatic Handling
OK	Record successfully extracted, validated, and normalized with zero null fields.
DEFAULT_PREFILL_APPLIED	When an empty input payload {} is submitted, the Actor automatically applies its built-in default query so 1-Click runs always succeed.
RATE_LIMIT_RETRY	Transient HTTP 429 / 503 responses trigger automatic retry with exponential backoff and session rotation.
INVALID_TARGET_SKIPPED	Unreachable or malformed target items in a batch are logged with [WARN] and skipped without interrupting the run.

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❓ Frequently Asked Questions (FAQ)

How much does scraping LinkedIn Jobs cost?

LinkedIn Jobs Scraper uses transparent Pay-Per-Event (PAY_PER_EVENT) pricing set 10x cheaper than the market leader ($\div 10$):

• Event Rate: $0.0006 per Scraped LinkedIn Jobs Scraper Record ($0.6 per 1,000 items).
• 🎁 1st Run 100% FREE: Your very first execution is 100% free ($0.00 event charges are applied on your initial run).
• Apify Free Plan ($5.00/month included): Every Apify account receives $5.00 in free monthly credits, which is enough to extract up to ~8,333 items per month for free.
• View full details on the Pricing tab and the Apify Pricing page.

Can I integrate LinkedIn Jobs Scraper with Make, Zapier, n8n, or Google Sheets?

Yes. LinkedIn Jobs Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform, including Make, Zapier, n8n, Slack, Airbyte, Google Sheets, Google Drive, and webhooks.

Can I use LinkedIn Jobs Scraper with the Apify API (Python & Node.js)?

Yes. The Apify API gives you programmatic access to run LinkedIn Jobs Scraper, schedule jobs, and fetch datasets. Use the apify-client PyPI package for Python or the apify-client NPM package for Node.js, or check the Apify API Reference:

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run = client.actor("rbbscrap/linkedin-jobs-scraper").call(run_input={
    "queries": ["Software Engineer"],
    "location": "France",
    "under10Applicants": False,
    "postedDate": "anyTime"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

Node.js (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_API_TOKEN' });

const run = await client.actor('rbbscrap/linkedin-jobs-scraper').call({
    queries: ["Software Engineer"],
    location: "France",
    under10Applicants: false,
    postedDate: "anyTime"
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Can I use LinkedIn Jobs Scraper through an MCP Server (Claude Desktop, Cursor, AI Agents)?

Yes. With the Apify API, you can use LinkedIn Jobs Scraper in conjunction with an MCP server to connect directly to Claude Desktop, Cursor, Windsurf, or LibreChat. Read the official guide on how to set up Apify Actors with MCP, or go to the MCP tab:

1. Start a Server-Sent Events (SSE) session to receive a sessionId.
2. Send API messages using that sessionId to trigger LinkedIn Jobs Scraper with your input parameters.
3. Receive structured LinkedIn Jobs dataset records directly inside your AI agent conversation.

Do I need residential proxies or login cookies to scrape LinkedIn Jobs?

No. LinkedIn Jobs Scraper operates on a zero-browser HTTP/2 protocol engine (256 MB RAM) and works out-of-the-box without requiring login cookies or external proxies for standard workloads. For high-volume enterprise runs, you can optionally use Apify Proxy in the input configuration.

Is it legal to scrape data from LinkedIn Jobs?

Our scrapers are ethical and do not extract any private user data behind login walls. They only extract what is shared publicly on the web. However, please keep in mind that personal data is protected by GDPR (EU Regulation 2016/679) and other regulations worldwide. You should not scrape personal data unless you have a legitimate legal basis. Read more in Apify's guide on the legality of web scraping.

Your feedback & support

We are continuously improving the performance of our Actors. If you have any technical feedback for LinkedIn Jobs Scraper, need a custom feature, or found a bug, please create an issue on the Actor's Issues tab in the Apify Console.