LinkedIn Jobs Scraper - Search & Export

Search and export public LinkedIn jobs by keyword, location, experience level, job type, Easy Apply, or public search and individual job URLs.

Use this Actor to turn LinkedIn Jobs searches into clean recruiting, labor-market, sales, and job-board datasets. Results can be downloaded as CSV, JSON, Excel, XML, RSS, or used through the Apify Dataset API.

Automate job-match scoring in n8n: Use the ready-made Score LinkedIn job matches with OpenAI, Google Sheets, Slack, and Apify workflow to run this Actor, rank job postings with AI, and send matches to your team.

Quick start

Use a focused search and a low cap. This example adds the documented experience-level and employment-type filters.

{
  "keywords": [
    "software engineer"
  ],
  "location": "United States",
  "maxItems": 10,
  "includeDetails": true,
  "datePosted": "pastWeek",
  "workplaceType": "remote",
  "experienceLevels": [
    "entry",
    "associate"
  ],
  "jobTypes": [
    "fullTime"
  ],
  "sortBy": "recent"
}

Example output

Shortened illustrative record, not a current job listing. Optional fields may be null.

{
  "jobId": "4374834620",
  "title": "Software Engineer (New Grads)",
  "companyName": "Giga",
  "companyUrl": "https://www.linkedin.com/company/gigaml",
  "location": "New York, NY",
  "postedAtText": "5 days ago",
  "jobUrl": "https://www.linkedin.com/jobs/view/software-engineer-new-grads-at-giga-4374834620",
  "description": "About Giga...",
  "employmentType": "Volunteer",
  "seniorityLevel": "Not Applicable",
  "industries": "Software Development",
  "applicantsText": "Over 200 applicants",
  "sourceSearchUrl": "https://www.linkedin.com/jobs-guest/jobs/api/seeMoreJobPostings/search?...",
  "scrapedAt": "2026-06-17T11:13:45.322Z"
}

Input settings

Filter and URL behavior

datePosted, workplaceType, experienceLevels, jobTypes, easyApplyOnly, and sortBy configure keyword-generated searches. When supplying a prebuilt search URL, keep the desired filters in that URL. For URL-only collection, send keywords: []. Direct job URLs do not become a fresh filtered search.

includeDetails: false keeps collection to search-card fields. When a detail request fails, a previously collected search-card row can still be saved with detailFetched: false. Check RUN_SUMMARY for failures and partial coverage. A rate-limit response pauses new requests across all workers, including when that request has exhausted its retries. Server Retry-After delays are respected when they fit within the safe run deadline; otherwise the run preserves available rows and reports partial coverage. The request time limit includes proxy selection, connection acquisition, setup and response. Shared pacing is checked when a prepared connection is handed to the HTTP client, so delayed proxy selection cannot bypass a later cooldown. Expected pacing waits do not consume the transport timeout or retries; the safe run deadline still applies. Aggregate admission time records request-capacity and final pacing waits, while setup time excludes them. Successful direct connections may be reused within the concurrency limit. Proxy requests open a fresh connection for each attempt so a rotating gateway can select a route even when its URL stays the same. Failed or rate-limited requests discard their connection before retrying; idle connections expire and all connections close at run completion. RUN_SUMMARY includes aggregate request, wait, storage, CPU and deadline-skip counters; summed concurrent request time can exceed elapsed run time. Details are processed continuously across result pages within the selected concurrency limit. One next search page can load while current details finish; search and detail requests share the existing connection capacity. Waiting for that capacity precedes the request timeout, with the safe run deadline still enforced. Empty or repeated pages, the requested result limit and billing limits stop further collection. Pending page requests are cancelled and drained before the source finishes. Collection can continue during dataset writes, with a bounded queue and serialized billing. Retry and pacing controls can reduce request pressure but do not guarantee access. The default run memory is 128 MB; explicit run memory settings take precedence.

LinkedIn controls how public searches interpret filters, and search behavior can change. Treat requested filters as search constraints, not a guarantee that every returned job matches. Verify returned fields before using the dataset for exact eligibility rules.

At a glance

• Public job search: Search LinkedIn Jobs by role, skill, company keyword, location, date posted, workplace type, and sort order.
• Search URL reuse: Paste a public LinkedIn Jobs search URL when you already built the right search in the browser.
• Detail enrichment: Optionally fetch job descriptions, seniority, employment type, industries, and applicant text from public job pages.
• Hiring intelligence: Monitor hiring demand by company, role, region, seniority, skill, and remote or hybrid status.
• API export: Send job rows to spreadsheets, CRMs, BI tools, job boards, matching pipelines, or AI agents.

What can it do?

LinkedIn Jobs Scraper collects public job posting data from LinkedIn Jobs and saves one dataset row per job posting.

• Find matching roles: Enter one or more keywords such as software engineer, data analyst, nurse, or sales development representative.
• Filter by market: Add a location, date-posted option, workplace type, and sort order.
• Collect public details: Enable detail fetching when you need descriptions, criteria, industries, seniority, and applicant text.
• Deduplicate results: The Actor deduplicates by LinkedIn job ID across searches and start URLs.
• Export clean rows: Use the Apify UI, Dataset API, integrations, schedules, and webhooks.

Weekly competitor hiring intelligence with AI

Turn public job listings into a repeatable competitor brief:

1. Save one focused search per competitor, role family, or region and run it weekly with an Apify schedule.
2. Keep includeDetails: true when an AI model needs descriptions, seniority, employment type, or industry context—not only job counts.
3. Send the finished dataset through an Apify webhook or API to n8n, a warehouse, or your AI workflow.
4. Compare the current run with the previous snapshot, then ask the model to group roles by function, summarize new locations and skills, and cite each finding with jobUrl.
5. Route the brief to a dashboard or an approved internal channel. Keep the raw rows for verification instead of relying on an uncited summary.

Choose the right source: Start here for broad public LinkedIn search by keyword and location. Add ATS Jobs Scraper for normalized employer-hosted boards across six ATS platforms, or Workday Jobs Scraper for named companies whose career sites run on Workday. These Actors produce separate datasets; join them downstream using company, title, location, and job URL rather than assuming identical schemas.

Turn hiring into account research

Monitoring jobs for sales signals? Pair this Actor with LinkedIn Public Company Profiles Scraper for firmographics, then Website Contact Finder for public business contact evidence. Join separate datasets by reviewed company URL or website domain; keep each job URL as the source for the hiring signal.

Common workflows

• Recruiting research: Build lists of roles, companies, locations, and job URLs for sourcing and market mapping.
• Lead generation: Find companies hiring for roles that indicate buying intent, growth, or an active initiative.
• Labor-market analysis: Compare demand across locations, job titles, workplace types, or posting freshness.
• Job-board ingestion: Export public LinkedIn job rows for review before adding them to downstream workflows.
• Competitive tracking: Monitor target companies or role families on a daily or weekly schedule.
• Student or job-seeker research: Export matching roles into a table for comparison and follow-up.

How to run it

1. Open the Actor on Apify.
2. Enter one or more keywords, a location, or public LinkedIn Jobs search URLs.
3. Set maxItems to the number of jobs you want to save.
4. Choose date, workplace, sort, and detail options.
5. Start the run.
6. Download the dataset or connect it to your workflow.

Search tips

• Start specific: Use role names like backend engineer, account executive, or data analyst instead of one broad word.
• Use one region per run: Separate locations make comparison and deduplication easier.
• Monitor freshness: Use sortBy: "recent" and datePosted: "past24h" or pastWeek for alerts.
• Control cost: Keep maxItems low while testing and increase it after the search returns relevant roles.
• Fetch details only when needed: Disable includeDetails when company, title, location, and URL are enough.

Limits and caveats

• The Actor extracts publicly visible LinkedIn Jobs data.
• It does not access private recruiter data, logged-in-only data, messages, applicants, or private profiles.
• LinkedIn may change page structures or rate-limit requests, so very large or broad runs should be split into focused searches.
• Some optional fields are empty when LinkedIn does not show them publicly for a job.

Integrations

You can connect the dataset to downstream tools:

• Export CSV or Excel to spreadsheets for recruiting reports.
• Send company lists to a CRM for lead prioritization.
• Use Apify schedules for daily or weekly hiring monitoring.
• Trigger webhooks when a run finishes.
• Pull results into a database, warehouse, BI dashboard, matching model, or AI agent.

Privacy and data handling

This Actor runs with Apify limited permissions and only processes data needed for the documented run. It uses job-search inputs and public job-posting results to produce the output dataset and sends requests to public Linkedin Jobs pages/endpoints; results are stored in Apify run storage for your account. FetchCat does not use your inputs or outputs for advertising, does not use them for model training, and does not retain them outside the Apify run except for transient support debugging when you explicitly share run details. You are responsible for using the Actor lawfully, respecting the target site's terms, and avoiding unnecessary personal or sensitive data in inputs.

API usage

Use your Apify API token through the APIFY_TOKEN environment variable. Node.js and Python examples wait for the run and read its first dataset page; paginate the dataset for larger exports.

Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/linkedin-jobs-scraper').call({
  "keywords": [
    "software engineer"
  ],
  "location": "United States",
  "maxItems": 10,
  "includeDetails": true,
  "datePosted": "pastWeek",
  "workplaceType": "remote",
  "experienceLevels": [
    "entry",
    "associate"
  ],
  "jobTypes": [
    "fullTime"
  ],
  "sortBy": "recent"
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import json
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run_input = json.loads('''{
  "keywords": [
    "software engineer"
  ],
  "location": "United States",
  "maxItems": 10,
  "includeDetails": true,
  "datePosted": "pastWeek",
  "workplaceType": "remote",
  "experienceLevels": [
    "entry",
    "associate"
  ],
  "jobTypes": [
    "fullTime"
  ],
  "sortBy": "recent"
}''')
run = client.actor("fetch_cat/linkedin-jobs-scraper").call(run_input=run_input)
print(client.dataset(run["defaultDatasetId"]).list_items().items)

cURL

Save the quickstart JSON as input.json. This request starts a run asynchronously; use its returned run ID to check completion and its defaultDatasetId to retrieve results.

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~linkedin-jobs-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  --data-binary @input.json

MCP and AI agents

Use the official Apify MCP server, not a separate custom server. The focused URL below selects this Actor. Authenticate with Apify when your client prompts you; configuration syntax and OAuth support depend on the client.

This Actor is part of the Jobs & Hiring Radar MCP workflow kit.

Claude Code

claude mcp add --transport http apify "https://mcp.apify.com?tools=fetch_cat/linkedin-jobs-scraper"

HTTP-capable MCP client configuration

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/linkedin-jobs-scraper"
    }
  }
}

Example prompt: "Export up to 10 entry-level remote software engineering jobs in the United States from the past week. Include source URLs and distinguish missing details from zero values."

Use the same input keys as the input table. Review the returned source URLs and any error or availability fields before using results in an automated summary.

Support

If a run fails or output looks wrong, open an issue from the Actor page. Include the Apify run ID or run URL, non-sensitive input JSON, expected output, actual output, and one reproducible public URL (or the exact search input). Do not share tokens, cookies, passwords, or private data.