🚀 LinkedIn Jobs Scraper – Scrape LinkedIn Jobs to JSON, CSV & Excel

Scrape and aggregate job listings from LinkedIn (linkedin.com) search results – job title, company, location, salary, posting date and a direct apply link – and export them to JSON, CSV, Excel or an API in a single run.

View on Apify Store

📖 Overview

The LinkedIn Jobs Scraper is a data-extraction tool that turns LinkedIn job search results into structured data. LinkedIn is the professional network's jobs section, with millions of active postings from companies worldwide.

Whether you are building a job board, running labour-market research, tracking a competitor's hiring, or feeding an AI agent with live job data, this Actor returns clean, ready-to-use results in real time – no browser automation or manual copy-paste on your side.

✨ Key Features

• 🔍 Full-Text Job Search – Search LinkedIn by keyword, job title or skill and walk every result page automatically.
• 🎯 Precise Filtering – Narrow results by country, city or region, company name, employment type and how recently a job was posted.
• 📅 Fresh Data – Pull only jobs posted today, in the last 3 days, this week or this month.
• 🧱 Structured Output – Every listing is parsed into consistent fields you can sort, filter and export.
• ⚡ Fast & Low-Cost – Optimised for speed and minimal compute so runs stay cheap.
• 🔌 API & No-Code Ready – Call it from Python, Node.js, cURL, Zapier, Make, or an MCP client.

🔌 Use this API from Claude (MCP)

Add this Actor as a tool in Claude Code (free trial), Claude Cowork (free trial), Cursor, or any other MCP client, via the hosted Apify MCP server. Use this Actor-specific URL:

https://mcp.apify.com/?tools=actors,docs,orgupdate/linkedin-jobs-scraper

Setup walkthrough:

Apify MCP integration docs: https://docs.apify.com/platform/integrations/mcp

With the server connected, your agent can search LinkedIn listings by title and city, compare postings across employers, and pull salary and apply links into a single structured answer.

💸 Pay per result

This Actor uses pay-per-event pricing. You are charged per job result returned, plus a tiny automatic actor-start fee (about $0.0001) per run. There is no monthly subscription and no charge for a run that returns nothing – see the FAQ below.

Pay per run with crypto (x402)

This API supports agentic payments via the x402 protocol. AI agents and MCP clients can pay for runs in USDC (on Base) with no Apify account or API token required: point your agent at the Apify MCP server and it can discover, pay for, and run this Actor autonomously. See the Apify x402 announcement for details.

🛠 Input Parameters

The Actor accepts the following input parameters in JSON format.

Parameter	Type	Required	Description	Default	Valid Values
includeKeyword	String	Yes	Search terms, job title or skills. Comma-separated values are supported.	-	Any string (e.g. "Product Manager", "React, Node.js")
locationName	String	Yes	City, state or region to search in.	-	Any location string (e.g. "London", "New York, NY")
countryName	String	Yes	Country context for the search.	"all"	"usa", "uk", "india", "all", etc.
pagesToFetch	Integer	Yes	Number of result pages to walk.	1	Positive integers
companyName	String	No	Restrict results to a single employer (e.g. "Microsoft").	-	Any company name
jobType	String	No	Filter by employment type.	-	FULLTIME, PARTTIME, CONTRACTOR, INTERN
datePosted	String	No	How recent the jobs should be.	"all"	all, today, 3days, week, month

💻 Example Input

{
  "includeKeyword": "software engineer, python",
  "locationName": "new york",
  "countryName": "usa",
  "companyName": "Microsoft",
  "jobType": "FULLTIME",
  "datePosted": "week",
  "pagesToFetch": 3
}

📊 Output Data

Results are stored in the default Apify dataset and can be downloaded as JSON, CSV, Excel, XML or HTML, or pulled straight from the dataset API.

Sample JSON Output

[
  {
    "job_title": "Senior Frontend Developer",
    "company_name": "Tech Corp Inc.",
    "location": "New York, NY (Remote)",
    "posted_via": "LinkedIn",
    "salary": "$120,000 - $150,000 a year",
    "date": "2025-03-25",
    "job_type": "Full-time",
    "URL": "https://linkedin.com/..."
  }
]

Field Dictionary

• job_title – The role title as published by the employer.
• company_name – The organisation hiring for the role.
• location – City / region, or Remote where stated.
• posted_via – The board or source the posting was found on.
• salary – Pay or pay range, when the employer provides one.
• date – The posting date.
• job_type – Full-time, part-time, contract or internship, when stated.
• URL – Direct link to the job listing / application page.

Not every LinkedIn listing includes every field – salary, job_type and exact date vary by posting – so handle optional fields gracefully in your integration.

🧩 How to Use

1. Run on Apify – Open the Actor in Apify Console, fill in the input form and click Start.
2. Schedule & automate – Set the Actor to run on a schedule to keep a job database fresh.
3. Export or integrate – Send results to your app, CRM, spreadsheet or data warehouse.

👨‍💻 Programmatic Usage

Run this Actor from the Apify API with the official clients. Replace orgupdate/linkedin-jobs-scraper with the real Actor ID from the Actor page.

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "includeKeyword": "Data Scientist",
    "locationName": "London",
    "countryName": "uk",
    "datePosted": "3days",
    "pagesToFetch": 1,
}

run = client.actor("orgupdate/linkedin-jobs-scraper").call(run_input=run_input)

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

Node.js

import { ApifyClient } from 'apify-client';

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

const input = {
    includeKeyword: 'DevOps',
    locationName: 'Berlin',
    countryName: 'germany',
    jobType: 'CONTRACTOR',
    pagesToFetch: 2,
};

const run = await client.actor('orgupdate/linkedin-jobs-scraper').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => console.dir(item));

🔗 Integrations

Don't just scrape data – act on it:

• Zapier / Make – Trigger an email, Slack or database action whenever a new matching job appears.
• Google Sheets – Append new job listings to a spreadsheet for tracking and analysis.
• Slack / Discord – Post new openings to a channel for your team or community.
• n8n – Add this Actor to any n8n workflow with the Apify node (or the HTTP Request node), including as an AI-Agent tool.

🗂 Featured Tasks

Ready-to-run configurations that solve a specific problem. Save any of these as a Task on your account and run it in one click.

• Find remote LinkedIn jobs via MCP – let an AI agent pull remote listings with company, source and direct apply links.
• Track Microsoft's open roles – monitor one employer's postings on LinkedIn on a daily schedule.
• Jobs posted in the last 3 days – recent LinkedIn listings filtered by datePosted, feeding a database.
• Salary benchmark by city – aggregate LinkedIn pay ranges for one job title across multiple locations.

💡 Use Cases

• Job boards & aggregators – Populate a niche board with fresh LinkedIn listings, no manual entry.
• Recruitment & sourcing – See which companies are hiring for which roles, and where.
• Market research – Track hiring demand, titles and salaries across regions over time.
• Lead generation – Find companies actively hiring so you can time an outreach.

❓ FAQ / Troubleshooting

No results returned? Check that includeKeyword is not empty, use a job-title style query (e.g. "Registered Nurse") rather than a long descriptive phrase, verify the spelling of locationName and countryName, and try removing the jobType / datePosted filters. Increasing pagesToFetch also helps on thin markets.

Am I charged for a run that returns no jobs? No. Pricing is pay-per-result, so a run that finds nothing only costs the automatic actor-start fee (about $0.0001), whatever the cause – a narrow query, a thin local market or an over-tight filter.

Fewer results than expected? Some queries genuinely have few listings on LinkedIn. Broaden the keyword (e.g. "engineer" instead of "senior backend engineer"), widen datePosted, and raise pagesToFetch.

Getting duplicates on a daily schedule? Set datePosted to today or 3days so each run only returns recent postings, and de-duplicate on the URL field when writing to your database.

🌐 Where the Data Comes From

Every row this Actor returns starts as a public listing on LinkedIn (linkedin.com). Employers and job boards publish those postings; this Actor reads the search results and parses them into fields. This is an independent tool. It is not affiliated with, endorsed by, or operated by LinkedIn, and it does not use a private LinkedIn API.

How is this different from searching LinkedIn by hand?

Browsing LinkedIn gives you one page of cards at a time and makes you keep loading more. This Actor walks the pagination for you and returns the whole set in one run, already parsed into job_title, company_name, location, salary, date and URL – fields you can sort, filter and export. An afternoon of scrolling and copy-paste becomes a run of a few minutes.

Does LinkedIn have a public jobs API?

Not one that returns search results you can reuse this way. That gap is what this Actor fills: pass a keyword and a location and you get structured JSON back.

🏢 About

This Actor is part of Orgupdate, a suite of job-market data tools and APIs covering the major job boards, aggregators and ATS platforms. For support or feature requests, open a ticket from the Actor page or the Apify Console.

Happy scraping! 🚀