LinkedIn Jobs Scraper 💼

foXLabs LinkedIn series: Company profiles · Company 360 · Hiring signals · Ad tracker · Ad discovery

🎉 Turn public LinkedIn jobs into clean, structured data — no login, no API key, one row per posting, with the title, company, location, posted date, seniority and description. Built for recruiters, sales (hiring-intent) and labour-market research.

🔍 What is the LinkedIn Jobs Scraper — and when should you use it?

Give this actor keywords, titles or company names and it returns matching jobs from public LinkedIn job postings — as clean, deduplicated rows you can filter, export or feed to an AI agent. Every run queries the source live, so the data is as fresh as the registry itself.

Use it when you need: a LinkedIn job company list for outreach; formation / status monitoring; or a canonical registry record for KYB and due diligence.

Use something else when: you need a company’s full ATS board — use the matching Fox Labs ATS actor for that.

🤖 Use with AI agents

Already on the Apify MCP server? Ask for this Actor by name: foxlabs/linkedin-jobs-scraper.

Your agent can pay for its own runs. This Actor is pay-per-event with agentic payments, so an agent can discover it, run it and settle the bill over x402 (USDC on Base) or Skyfire — no Apify account or API token of its own. Billing is the same either way: per delivered record, never for errors.

Otherwise paste this into Claude, ChatGPT, Cursor or any MCP-enabled assistant:

I want to pull LinkedIn job company records using the Apify Actor `foxlabs/linkedin-jobs-scraper`.
Input: `queries` is a list of keywords, titles or company names. `maxResultsPerQuery` caps rows per query.
Start with: {"queries":["undefined"],"maxResultsPerQuery":50}
Ask me what to look up, run the Actor, then summarise the rows as a table.

The machine-readable API, MCP config and OpenAPI definition live at apify.com/foxlabs/linkedin-jobs-scraper.md.

📋 Overview

Everything you need to turn public LinkedIn job postings into clean, structured data — in one actor, with no login, cookies or API key.

Why teams pick this actor:

• ✅ Whole source, one call — name or ID in, matching jobs out.
• 🧹 No empty-promise columns — only fields this registry actually fills; degenerate columns are removed.
• 🔗 Stable identifiers — every row carries the source's own IDs, ready to join across runs and to other Fox Labs actors.
• 💰 Pay only for results — per-row pricing, empty/failed lookups never billed.
• 🤖 Agent-ready — MCP + x402 agentic payments.

✨ Features

• 🔍 Name or ID lookup — relevance-ranked name search or exact registry-ID lookup.
• 🏢 Full entity profile — status, legal form, formation date, address and the registry’s own contact fields.
• 🧹 Clean schema — deduplicated camelCase rows, ready for CSV/Excel/JSON.

🎬 Quick Start

curl -X POST "https://api.apify.com/v2/acts/foxlabs~linkedin-jobs-scraper/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries":["undefined"],"maxResultsPerQuery":50}'

🚀 Getting Started (3 steps)

1. Choose your targets — keywords, titles or company names.
2. Set the cap — maxResultsPerQuery limits rows per query.
3. Run and export — get a clean dataset as JSON, CSV or Excel.

📥 Input

{"queries":["undefined"],"maxResultsPerQuery":50}

Field	Type	Description
queries	array	Keywords, titles or company names.
maxResultsPerQuery	integer	Caps rows per query.
maxConcurrency	integer	How many queries to fetch at once.
includeRaw	boolean	Attach the source’s untouched record under raw.

📤 Output

One row per company, saved to the dataset. Every row also carries query, scrapedAt, and — when a lookup fails — an error explaining why (never silently dropped, never billed).

Field	Description
jobId	Job Id
jobUrl	Job Url
title	Title
companyName	Company / entity name
companyLinkedInUrl	Company Linked In Url
companySlug	Company Slug
companyLogo	Company Logo
location	Location
postedDate	Posted Date
postedTimeAgo	Posted Time Ago
listingBadge	Listing Badge
descriptionText	Description Text
descriptionHtml	Description Html
seniorityLevel	Seniority Level
employmentType	Employment Type
jobFunction	Job Function
industries	Industries
applicantCount	Applicant Count
applicantCountText	Applicant Count Text
applicantCountIsUpperBound	Applicant Count Is Upper Bound
companyEmployeeCount	Company Employee Count
companyFollowers	Company Followers
companyIndustry	Company Industry
source	Source
companyWebsite	Company Website
companyDomain	Company Domain
companyHQ	Company H Q
salaryRaw	Salary Raw
salaryMin	Salary Min
salaryMax	Salary Max
salaryCurrency	Salary Currency
salaryPeriod	Salary Period
salaryFormatted	Salary Formatted

💼 Use cases

1. Hiring-intent signals — spot companies hiring for a function. Input: keywords + location. Output: jobs by company. Use: rank accounts by intent.

2. Recruiter sourcing — find open roles across companies. Input: titles or keywords. Output: postings + apply links. Use: a sourcing list.

3. Labour-market research — track demand for a role over time. Input: keywords, scheduled. Output: postings over time. Use: a demand trend.

🔗 Integration

JavaScript / Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('foxlabs/linkedin-jobs-scraper').call({"queries":["undefined"],"maxResultsPerQuery":50});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')
run = client.actor('foxlabs/linkedin-jobs-scraper').call(run_input={"queries":["undefined"],"maxResultsPerQuery":50})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

Automation (n8n / Zapier / Make): schedule or webhook → HTTP request to the actor API with your queries → handle the JSON dataset → push to a sheet, CRM or dashboard.

📊 Pricing

Pay-per-event: per delivered record. Empty or failed lookups are never billed. View current pricing.

❓ FAQ

Do I need an account, login or API key? No. This reads public LinkedIn job postings.

What do I search by? Keywords, titles or company names.

How current is the data? Every run queries the source live, so results are as fresh as the registry.

What job fields are returned? Title, company, location, posted date, seniority, employment type and description from the public LinkedIn posting.

Can I export to CSV / Excel / JSON? Yes — directly from the Apify dataset.

🐛 Troubleshooting

• Fewer rows than expected — raise maxResultsPerQuery, or refine the name.
• A name returns an unexpected entity — it matched a similar registered name; search the exact registry ID.
• No rows for a name — try the entity’s exact legal name or its registry ID.

⚖️ Is it legal to scrape this data?

This actor reads publicly available LinkedIn job postings. Results can still contain personal data (e.g. a person’s name); personal data is protected by the GDPR and similar laws, so only process it with a legitimate basis. See Apify’s blog post on the legality of web scraping.

🤝 Support & contact

• 🌐 Website: data.foxlabs.com.tr
• 📧 Email: info@foxlabs.com.tr
• 🐛 Issues: open a ticket in the Actor’s Issues tab
• 🧰 More clean B2B data actors: Fox Labs on Apify

Changelog

0.1 — 2026-09-07

• Enabled AI-agent payments (x402) + rebuilt the README to the full standard (What-is / when, AI-agents + x402 agentic payments + MCP, Overview, Features, Use cases, Integration, FAQ, Troubleshooting, Support & contact).

0.0

• Initial release: data from public LinkedIn job postings by name or registry ID.