LinkedIn Jobs Scraper searches LinkedIn job postings by keyword and location without logging in, and returns title, company, location, salary when posted, date, seniority, employment type, industries, applicant count and the full job description. It is a LinkedIn job scraper for job boards, recruiters, sales teams and labor market researchers. Use it to export LinkedIn jobs to Excel, CSV or JSON with no LinkedIn account, no cookies and no risk to your profile.

What does LinkedIn Jobs Scraper do?

You type a search the same way you would on LinkedIn Jobs: keywords and a location, plus optional filters for date posted and workplace type. The scraper reads LinkedIn's public job search results page by page (10 jobs per page) until it reaches your limit or the end of the results. Then, for each job, it can open the public job page to add the full description and the job criteria.

What you can put in the search:

Input	Field	Example
Job title, skill or company	keywords	data engineer
City, state or country	location	United States, São Paulo
Date posted	datePosted	24h, week, month or any
Workplace type	remote	onsite, remote, hybrid or any

Each job becomes one row with the card data (job ID, title, company and its LinkedIn page, location, salary when shown, date posted, job link) and, with details on, the description as plain text and HTML, seniority level, employment type, job function, industries and the applicant count text. You do not need a LinkedIn account, cookies or an API key.

What data can you extract?

Field	What it means	Example
jobId	LinkedIn job ID	4465619627
title	Job title	Senior Data Engineer
company	Hiring company name	Deloitte
companyUrl	Company page on LinkedIn	https://www.linkedin.com/company/deloitte
location	Job location as shown on LinkedIn	Washington, DC
salary	Salary range from the search card, only when the employer posts one	null
postedAt	Date the job was posted (YYYY-MM-DD)	2026-10-01
url	Link to the job posting	https://www.linkedin.com/jobs/view/databricks-engineer-…-4465619627
description	Full job description as plain text (details only)	Our Deloitte AI & Engineering team to transform technology platforms…
descriptionHtml	Full job description with its HTML formatting (details only)	Our Deloitte AI & Engineering team…
seniority	Seniority level (details only)	Mid-Senior level
employmentType	Employment type (details only)	Full-time
jobFunction	Job function, when LinkedIn lists it (details only)	null
industries	Industries of the job (details only)	IT Services and IT Consulting
applicants	Applicant count text (details only)	Be among the first 25 applicants
scrapedAt	When the row was collected	2026-10-07T19:36:49.826Z

Examples come from the latest run. Fields marked "details only" are filled when Include full description is on (the default). Empty values come as null.

How to use LinkedIn Jobs Scraper

1. Open LinkedIn Jobs Scraper in Apify Console (sign in or create a free Apify account).
2. Type Keywords (for example data engineer) and a Location (for example United States or São Paulo).
3. Optional: pick Date posted (past 24 hours, past week, past month or any time) and Workplace type (on-site, remote, hybrid or any).
4. Set Max jobs (default 25, up to 1,000).
5. Keep Include full description on for description, seniority, employment type and applicants, or turn it off for a faster run with card data only.
6. Click Start, then open the Output tab and click Export to download the data as JSON, CSV, Excel or HTML.
7. Optional: save the input as a task and schedule it daily with Date posted set to past 24 hours to collect new jobs every day.

Input

Field	Type	Default	Description
keywords	string	data engineer	Job title, skill or company, for example data engineer.
location	string	United States	City, state or country, for example United States, São Paulo.
datePosted	string: any, 24h, week, month	week	Only jobs posted in that period. any turns the filter off.
remote	string: any, onsite, remote, hybrid	any	Workplace type. any turns the filter off.
maxItems	integer, 1 to 1,000	25	Max jobs to return.
includeDetails	boolean	true	Open each job to get description, seniority, employment type and applicants.
proxy	object	{"useApifyProxy": true}	Apify Proxy (datacenter works well).

Example input for remote data engineer jobs in the United States posted in the last 24 hours:

{
  "keywords": "data engineer",
  "location": "United States",
  "datePosted": "24h",
  "remote": "remote",
  "maxItems": 200,
  "includeDetails": true,
  "proxy": {
    "useApifyProxy": true
  }
}

Output example

A real row from the latest run (the description fields are shortened):

{
  "jobId": "4465619627",
  "title": "Databricks Engineer | Data Engineer II - Top Secret Clearance",
  "company": "Deloitte",
  "companyUrl": "https://www.linkedin.com/company/deloitte",
  "location": "Washington, DC",
  "salary": null,
  "postedAt": "2026-10-01",
  "url": "https://www.linkedin.com/jobs/view/databricks-engineer-data-engineer-ii-top-secret-clearance-at-deloitte-4465619627",
  "description": "Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients' success…",
  "descriptionHtml": "Our Deloitte AI & Engineering team to transform technology platforms, drive innovation, and help make a significant impact on our clients' success…",
  "seniority": "Not Applicable",
  "employmentType": "Full-time",
  "jobFunction": null,
  "industries": "Accounting, IT Services and IT Consulting, and Business Consulting and Services",
  "applicants": "Be among the first 25 applicants",
  "scrapedAt": "2026-10-07T19:36:49.826Z"
}

How much does it cost?

LinkedIn Jobs Scraper uses pay per event pricing. You pay $1.00 per 1,000 jobs returned ($0.001 per job) on the Free plan, with or without the full description. Platform usage (compute and proxy) is included in that price.

Apify plan	Price per 1,000 jobs
Free	$1.00
Bronze (10% off)	$0.90
Silver (20% off)	$0.80
Gold and higher (30% off)	$0.70

Worked examples on the Free plan:

• The default 25 jobs = $0.025
• One search with the maximum of 1,000 jobs = $1.00
• 5 searches × 100 new jobs every day for 30 days = 15,000 jobs = $15.00 ($10.50 on Gold)

What you are not charged for:

• Searches that return no jobs. There is no error row and nothing is charged.
• Search pages and job pages themselves. Only the job rows count.
• Duplicate jobs. A job ID that comes up again in later result pages is skipped.

Use cases

• Job boards and aggregators. Fill a niche job board with fresh LinkedIn listings every day using the past 24 hours filter.
• Recruiting and staffing. See which companies are hiring for a role in a city, how senior the roles are and how many applicants they already have.
• Sales prospecting. Use hiring as a buying signal: a company hiring data engineers may need data tools. Take company and companyUrl into your CRM.
• Labor market research. Count postings by location, seniority, employment type or industry over time.
• Salary research. Collect the salary ranges that employers publish on their cards.
• Remote work tracking. Compare the share of remote, hybrid and on-site jobs for a skill.
• Job alerts. Schedule a search with the past 24 hours filter and send the results to email, Slack or a spreadsheet through an integration or a webhook.

Tips and limits

• Up to 1,000 jobs per run. The scraper pages through LinkedIn's public search 10 jobs at a time, up to 1,000 results. For bigger needs, split the search by location, by datePosted or by more specific keywords.
• One search per run. keywords and location take one value each. Use several runs or tasks for several searches.
• Results end early sometimes. The run stops when LinkedIn returns no new jobs or a results page fails after 5 tries. You keep the jobs collected up to that point.
• Salary is often missing. salary comes from the search card and is only there when the employer published it. In recent runs it was empty for the jobs we checked, so do not count on it for every search.
• Job function is often empty. jobFunction is filled only when the job page lists it. In recent runs it was null for all jobs, while seniority, employmentType and industries were filled.
• Details can be missing on a few jobs. Each job page gets up to 3 tries. If it still does not load, the job is saved with the card data only (no description) and is still counted as a job.
• Plain text vs HTML. description has all whitespace collapsed into single spaces, so paragraphs run together. Use descriptionHtml to keep lists and line breaks.
• Same role, several locations. LinkedIn often posts the same role once per city with different job IDs, so they come as separate rows.
• Applicants is text. applicants is the text LinkedIn shows, like 26 applicants, Over 200 applicants or Be among the first 25 applicants.
• Faster runs. Turn off includeDetails when you only need titles, companies, locations and links.

Use it from the API, Python or JavaScript

JavaScript (Node.js) with the official apify-client package:

const { ApifyClient } = require('apify-client');

const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const input = { keywords: 'data engineer', location: 'United States', datePosted: 'week', maxItems: 100 };

(async () => {
    const run = await client.actor('juliob.rangel/linkedin-jobs-fast').call(input);
    const { items } = await client.dataset(run.defaultDatasetId).listItems();
    console.log(items);
})();

Python with the official apify-client package:

from apify_client import ApifyClient

client = ApifyClient('YOUR_TOKEN')
run_input = {'keywords': 'product manager', 'location': 'São Paulo', 'datePosted': '24h', 'remote': 'hybrid', 'maxItems': 50}

run = client.actor('juliob.rangel/linkedin-jobs-fast').call(run_input=run_input)
items = client.dataset(run['defaultDatasetId']).list_items().items
for item in items:
    print(item.get('title'), '|', item.get('company'), '|', item.get('location'), '|', item.get('url'))

The scraper also works with Make, Zapier and n8n, can export results to Google Sheets, can call your webhook when a run finishes, and can be used by AI agents through the Apify MCP server.

FAQ

Do I need a LinkedIn account or cookies?

No. The scraper uses LinkedIn's public job search and job pages, the same ones anyone sees without logging in. There is no risk to your own LinkedIn account. You only need an Apify account to run it.

Can I export LinkedIn jobs to Excel or CSV?

Yes. After the run, open the Output tab and export the dataset as Excel, CSV, JSON or HTML, or read it through the API.

How many jobs can I get?

Up to 1,000 per run with maxItems, as long as LinkedIn has that many results for your search.

Is it legal to scrape LinkedIn jobs?

The scraper only collects publicly available job postings. You are responsible for complying with LinkedIn's terms and with privacy laws such as GDPR and LGPD. Job postings rarely hold personal data, but if they do (for example a recruiter's name in the text), you need a legitimate reason to process it.

Why did my run return no jobs?

LinkedIn returned no results for that combination of keywords, location and filters, or its search did not answer after 5 tries. The scraper does not add an error row in that case, and nothing is charged. Try a broader location, datePosted set to any, or run it again later.

Does it get the poster's or recruiter's profile?

No. It returns job and company data only, not people profiles.

Can I get jobs from many countries in one run?

No. Each run uses one location. Save a task per country or city and run them together or on a schedule.

Related scrapers

• Career Site Jobs: Greenhouse, Lever, Ashby, Workable
• Remote Jobs Scraper (Himalayas, Remote OK, We Work Remotely)
• LinkedIn Company Scraper (No Login, Fast)
• Website Contact Details Scraper (Emails, Phones, Socials)
• Google Maps Email Extractor (Leads with Emails & Socials)

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