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.

Ready-to-run examples

Open a saved example to inspect its inputs, then adjust the limits and filters for your own run. Examples are starting points; source availability can change.

• Job Alert Dataset Export
• Part Time Developer Jobs
• Remote AI Product Jobs
• Industry Hiring Snapshot
• Competitor Hiring Monitor
• Location Salary Keyword Research

View all ready-to-run examples

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

Setting	JSON key	Type / default	What it does
Keywords	keywords	array / not set	Job titles, skills, company names, or search phrases. When neither keywords nor start URLs are supplied, the Actor searches for software engineer jobs for backward compatibility.
Location	location	string / not set	LinkedIn location text for keyword searches, such as United States, London, Berlin, or Remote.
LinkedIn jobs search or job URLs	startUrls	array / not set	Public LinkedIn Jobs search URLs or direct /jobs/view/ URLs. They are processed in addition to keyword searches; submit an empty keywords list to use only these URLs.
Maximum jobs	maxItems	integer / 20	Maximum unique job rows saved across all searches and direct URLs. Use a small value while testing to cap spend. Minimum 1; maximum 10000.
Fetch job details	includeDetails	boolean / true	Fetch each public job page for description text and HTML, criteria, applicant and salary text, application link, workplace type, and other fields. Search-card rows are preserved if individual detail pages fail.
Date posted	datePosted	string / "any"	Filter keyword searches using LinkedIn's public date-posted options. Values: any, past24h, pastWeek, pastMonth.
Workplace type	workplaceType	string / "any"	Filter keyword searches by on-site, remote, or hybrid roles. Values: any, onsite, remote, hybrid.
Experience levels	experienceLevels	array / []	Optional LinkedIn experience-level filters. Select any combination. Values: internship, entry, associate, midSenior, director, executive.
Job types	jobTypes	array / []	Optional LinkedIn employment-type filters. Select any combination. Values: fullTime, partTime, contract, temporary, volunteer, internship, other.
Easy Apply only	easyApplyOnly	boolean / false	Ask LinkedIn's public search endpoint to return only jobs marked for Easy Apply.
Sort by	sortBy	string / "relevance"	Choose LinkedIn relevance ranking or newest jobs first. Values: relevance, recent.
Retries per request	retryCount	integer / 3	Retries temporary blocks, rate limits, timeouts, TLS failures, and server errors with a fresh proxy route. Minimum 0; maximum 8.
Initial retry delay (milliseconds)	initialRetryDelayMillis	integer / 1000	Initial backoff before retrying a temporary request failure. Later retries wait longer and Retry-After is honored. Minimum 250; maximum 30000.
Request pacing (milliseconds)	requestPacingMillis	integer / 250	Delay between successful request batches. Increase it if a broad run is rate-limited. Minimum 0; maximum 10000.
Detail request concurrency	detailConcurrency	integer / 4	Maximum public job detail pages fetched at once. Lower values reduce request pressure; higher values finish enrichment faster. Minimum 1; maximum 12.
Safe run-time limit (seconds)	runTimeSecs	integer / 240	Stop requesting new pages early enough to preserve saved rows and write RUN_SUMMARY before the platform timeout. Minimum 30; maximum 270.
Proxy configuration	proxyConfiguration	object / {"useApifyProxy":true}	Apify Proxy is recommended for LinkedIn public pages. Temporary failures rotate to a fresh proxy URL automatically.

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. Retry and pacing controls can reduce request pressure but do not guarantee access.

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.

Output fields

JSON key	Type	Meaning
jobId	string	LinkedIn job posting identifier.
title	string / null	Public job title.
companyName	string / null	Public employer name.
companyUrl	string / null	Public LinkedIn company page when available.
companyId	string / null	Numeric LinkedIn company identifier when present in the public URL.
companyLogoUrl	string / null	Public company logo image URL when visible.
location	string / null	Location shown on the posting.
postedAtText	string / null	Relative posting-age text shown by LinkedIn.
postedAt	string / null	Normalized public posting timestamp when available.
jobUrl	string	Direct public LinkedIn job URL.
description	string / null	Clean public job description text when detail fetching succeeds.
descriptionHtml	string / null	Public job description markup when detail fetching succeeds.
employmentType	string / null	Employment type shown in job criteria.
seniorityLevel	string / null	Seniority level shown in job criteria.
industries	string / null	Industry text shown in job criteria.
jobFunction	string / null	Job function shown in job criteria.
workplaceType	string / null	On-site, remote, or hybrid label when visible on the detail page.
salaryText	string / null	Public compensation text when disclosed.
applicantsText	string / null	Public applicant-count text when visible.
applicantsCount	integer / null	Numeric portion of the public applicant-count text.
applyUrl	string / null	Public application destination when exposed by the page.
easyApply	boolean / null	Whether the public application control is labeled Easy Apply.
criteria	object	All public label/value job criteria found on the detail page.
sourceSearchUrl	string / null	Search or direct-job source that produced the row.
sourceKind	string	Whether the row came from a search page or direct job URL.
detailFetched	boolean	True when public detail enrichment succeeded.
scrapedAt	string	ISO timestamp when the row was parsed.

applicantsCount is a numeric extraction from public applicant text, not necessarily an exact total: for example, “Over 200 applicants” may produce 200. Keep applicantsText for context.

Pricing

The start event is charged once after input validation. The result event applies to each unique saved job row. A saved search-card row remains a billable result if optional detail enrichment fails; source errors and duplicate jobs are not additional job results. A valid empty run can still incur its start charge.

See the live Pricing tab for current rates and discounts. Check the cost shown for your account before scaling a run; any applicable platform usage is shown by Apify separately.

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.

FAQ

Can I scrape LinkedIn jobs without a LinkedIn account?

Yes. This Actor targets public LinkedIn Jobs pages and public job data.

Can I export LinkedIn jobs to CSV or Excel?

Yes. Apify datasets can be downloaded as CSV, JSON, Excel, XML, RSS, HTML, or accessed through the API.

Why are some detail fields empty?

LinkedIn does not show every field on every public job. Detail fields also require includeDetails to be enabled.

Can I run it every day?

Yes. Use Apify schedules and set a focused query, date filter, and moderate maxItems value.

Related scrapers

• Greenhouse Job Board Scraper
• Workday Jobs Scraper
• Lever Jobs Scraper
• SmartRecruiters Jobs Scraper
• RemoteOK Jobs Scraper

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.

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.