LinkedIn Job Search

Extract comprehensive LinkedIn job listings with 35+ rich data points per posting.

Unlock professional job intelligence: company ratings, employee counts, salary transparency, and verified employer data. Get detailed job descriptions, skill requirements, remote work options, and hiring team insights—all from the world's largest professional network with 1 billion+ members.

🤝 Support & Community

📧 Support: Contact Us 💬 Community: Telegram Group

🏆 Key Features

💼 Professional Job Intelligence

• 👥 Applicant Insights: Track real-time competition levels—identify high-demand roles vs. hidden opportunities with fewer applicants.
• 📊 Company Verification: Access verified company profiles with employee counts, growth trends, and industry classifications unique to LinkedIn.
• 💰 Salary Transparency: Extract disclosed salary ranges with currency and payment period details from employer postings.
• 🎯 Skills Taxonomy: Capture structured skill requirements aligned with LinkedIn's professional skills graph for precise job-candidate matching.

📈 Comprehensive Job Data

• 🏢 Rich Company Profiles: Company ratings, review counts, revenue ranges, headquarters locations, and official websites.
• 📍 Location Intelligence: Precise job locations with remote/hybrid/on-site work arrangement detection across 200+ countries.
• ⏰ Posting Freshness: Filter by posting date with flexible time ranges (24 hours to custom dates) for time-sensitive recruiting.
• 🔗 Direct Application Links: Both LinkedIn job URLs and direct company career page links for streamlined candidate pipelines.

🎨 Structured Output Format

• 📋 Normalized Schema: Consistent 35+ field structure across all job postings for easy integration and analysis.
• 🗓️ ISO Timestamps: Standardized date formats for posting dates and processing timestamps.
• 📝 Markdown Descriptions: Clean, formatted job descriptions ready for display or further processing.
• 🔢 Quantified Metrics: Numeric fields for salary ranges, vacancy counts, experience requirements, and company ratings.

💰 Pricing

Resource	Cost	Description
Actor Usage	$0.00001	Charged for Actor runtime, proxy and storage. Cost depends on resource consumption during execution
Job Details	$0.00252	Charged for each job posting extracted. Includes complete metadata with salary, skills, and company

Example Cost Calculation:

• Searching 2 keywords with 100 results each
• Cost: (200 jobs × $0.00252) = $0.504 + runtime fees

🎯 Use Cases

🎯 B2B Lead Generation from Job Postings

Companies hiring for specific roles signal intent, budget, and pain points. Extract job postings to identify companies actively investing in a technology, team, or initiative—then route company_website, emails, and social_links directly into your CRM for outreach.

search_terms: ["Salesforce Administrator", "HubSpot CRM Manager"]
country: "United States"  posted_since: "2 weeks"  max_results: 100
→ Company names, websites, emails, and LinkedIn URLs for targeted B2B prospecting

🔬 Talent Pipeline & Candidate Sourcing

Track which companies are hiring for the same roles you place candidates in. Use vacancy_count, job_level, and company_rating to prioritize outreach to employers with multiple openings and strong employee sentiment.

search_terms: ["Senior Frontend Engineer"]  country: "Germany"
location: "Berlin"  posted_since: "1 month"  max_results: 150
→ Active hiring companies with open headcount, seniority requirements, and company size

📊 Skills Demand Analysis & Workforce Intelligence

Aggregate the skills field across hundreds of postings to measure which technologies and competencies are trending in your target market. Feed results into a BI dashboard to track skills demand shifts quarter over quarter.

search_terms: ["Data Engineer", "Analytics Engineer"]  country: "United Kingdom"
posted_since: "1 month"  max_results: 200
→ Skills frequency table showing which tools (dbt, Spark, Snowflake) dominate job ads

🏠 Remote Work Trend Tracking

Monitor how remote and hybrid work adoption changes by role and region over time. Schedule weekly runs and compare is_remote and work_from_home distributions to produce labor market reports for clients or internal planning.

search_terms: ["Product Manager"]  country: "United States"
posted_since: "1 week"  max_results: 200
→ Remote vs. hybrid vs. on-site breakdown per posting for market trend analysis

📈 Company Growth Signals for Investment Research

Rapid hiring across multiple roles is a leading indicator of company expansion. Track employee_count, company_revenue, and new job volume over time to surface fast-growing companies before they appear in traditional financial data sources.

search_terms: ["Head of Sales", "VP Engineering", "Chief of Staff"]
country: "United States"  posted_since: "2 weeks"  max_results: 100
→ Executive hiring signals with company size, revenue range, and verified LinkedIn profiles

🌟 Why choose this Actor?

Built for recruiters, HR tech platforms, and labor market analysts, this Actor transforms LinkedIn's professional job ecosystem into actionable, structured data.

Feature	LinkedIn Job Search	Bright Data	Octoparse	ScraperAPI
Pricing Model	✅ Pay per use	❌ Enterprise	⚠️ Subscription	⚠️ Credit-based
Data Depth	✅ 35+ fields	⚠️ 20-25 fields	⚠️ 15-20 fields	⚠️ Basic only
Company Data	✅ Verified info	⚠️ Limited	⚠️ Basic only	❌ Not available
Salary Data	✅ Included	⚠️ Partial	❌ Not available	❌ Not available
Setup Time	✅ Instant	❌ Complex setup	⚠️ Template req.	⚠️ API setup
Min. Cost	✅ $0.03	❌ $500+/month	⚠️ $75/month	⚠️ $49/month

💻 Input Parameters

Parameter	Type	Required	Description
search_terms	string[]	✅ Yes	Job titles, skills, or company names to search. Each term creates a separate search query. Examples: "Product Manager", "Python Developer", "Google"
country	string	✅ Yes	Target country for job search from 95+ supported countries. Must match exact country name (e.g., "United States", not "USA")
location	string	❌ No	Optional city, metro area, or region to narrow search results. Examples: "San Francisco Bay Area", "New York City", "London". Leave empty for country-wide search
distance	integer	❌ No	Search radius in miles around the specified location. Default: 50 miles. Only applicable when location is provided
posted_since	string	✅ Yes	Filter jobs by posting date. Supports relative formats ("1 week", "30 days", "2 months") or absolute dates ("2024-12-01"). Flexible time range filtering
max_results	integer	✅ Yes	Maximum number of job listings to extract per search term. Minimum value: 10. Higher values increase runtime and cost proportionally

📝 Example Input

{
  "search_terms": ["Product Manager", "Software Engineer"],
  "country": "United States",
  "location": "San Francisco Bay Area",
  "distance": 25,
  "posted_since": "1 week",
  "max_results": 100
}

📤 Output Structure

Field	Type	Description
processor	string	URL of the Apify actor that processed this data
processed_at	string	ISO 8601 timestamp when the data was scraped (format: YYYY-MM-DDTHH:MM:SS+00:00)
platform	string	Source platform name (LinkedIn)
platform_url	string	Direct LinkedIn job posting URL
official_url	string	Company's official career page URL for this job (if available)
title	string	Job position title as posted by employer
posted_date	string	ISO 8601 timestamp when the job was originally posted
location	string	Job location (city, metro area, or "Remote")
is_remote	boolean	Whether the job allows remote work
description	string	Full job description in markdown format with responsibilities and requirements
job_type	string	Employment type (full-time, part-time, contract, temporary, internship)
job_level	string	Seniority level (entry, associate, mid-senior, director, executive)
job_function	string	LinkedIn job function category (e.g., Engineering, Marketing, Sales)
listing_type	string	Listing visibility type (standard, promoted, featured)
emails	array	Contact email addresses found in job posting (if any)
phones	array	Contact phone numbers found in job posting (if any)
social_links	object	Social media profile links for the hiring company
skills	string	Required skills and technologies extracted from LinkedIn's skill tags
work_from_home	string	Work arrangement (remote, hybrid, on-site)
vacancy_count	integer	Number of open positions for this role
experience_range	string	Required years of experience (e.g., "3-5 years", "5+ years")
salary_period	string	Payment frequency (yearly, monthly, hourly)
salary_minimum	number	Minimum salary amount in specified currency
salary_maximum	number	Maximum salary amount in specified currency
salary_currency	string	Currency code (USD, EUR, GBP, etc.)
company_name	string	Verified company name from LinkedIn company page
company_industry	string	LinkedIn industry classification (e.g., "Technology, Information and Internet")
company_url	string	LinkedIn company profile page URL
company_website	string	Official company website URL
company_logo	string	Company logo image URL from LinkedIn CDN
company_addresses	string	Company headquarters and office locations
company_revenue	string	Annual revenue range (e.g., "$100M-$500M", "$1B+")
company_description	string	Company "About" section from LinkedIn profile
company_rating	number	Company rating score out of 5.0 based on employee reviews
employee_count	string	LinkedIn employee count range (e.g., "1001-5000", "10001+")
review_count	integer	Total number of company reviews on LinkedIn

📤 Example Output

{
  "processor": "https://apify.com/cheapget/linkedin-job-search?fpr=aiagentapi",
  "processed_at": "2025-12-30T14:30:00+00:00",
  "platform": "LinkedIn",
  "platform_url": "https://www.linkedin.com/jobs/view/3847291056",
  "official_url": "https://careers.google.com/jobs/results/senior-product-manager",
  "title": "Senior Product Manager, Cloud AI",
  "posted_date": "2025-12-22T00:00:00+00:00",
  "location": "Mountain View, CA",
  "is_remote": false,
  "description": "Join Google Cloud's AI team to shape the future of enterprise AI products!\n\n## What you'll do\n- Define product roadmap for Cloud AI services\n- Partner with engineering to deliver world-class ML infrastructure\n- Drive go-to-market strategy with cross-functional teams\n- Analyze market trends and competitive landscape",
  "job_type": "full-time",
  "job_level": "senior",
  "job_function": "Product Management",
  "listing_type": "promoted",
  "emails": ["jobs@google.com"],
  "phones": ["+1-650-253-0000"],
  "social_links": {
    "linkedin": "https://www.linkedin.com/company/google",
    "twitter": "https://twitter.com/google",
    "facebook": "https://www.facebook.com/Google"
  },
  "skills": "Product Strategy, Machine Learning, Cloud Computing, Agile, SQL, Stakeholder Management",
  "work_from_home": "hybrid",
  "vacancy_count": 3,
  "experience_range": "5-8 years",
  "salary_period": "yearly",
  "salary_minimum": 180000,
  "salary_maximum": 260000,
  "salary_currency": "USD",
  "company_name": "Google",
  "company_industry": "Technology, Information and Internet",
  "company_url": "https://www.linkedin.com/company/google",
  "company_website": "https://google.com",
  "company_logo": "https://media.licdn.com/dms/image/C4D0BAQHiNSL4Or29cg/company-logo_200_200/google-logo.png",
  "company_addresses": "1600 Amphitheatre Parkway, Mountain View, CA 94043",
  "company_revenue": "$280B+",
  "company_description": "A technology company that specializes in Internet-related services and products, including online advertising, search engine, cloud computing, and AI.",
  "company_rating": 4.4,
  "employee_count": "100001+",
  "review_count": 45000
}

🔌 Integrations

Seamlessly connect this actor to your existing pipelines via the Apify API.

🐍 Python SDK

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run_input = {'search_terms': ['Product Manager', 'Software Engineer'],
 'country': 'United States',
 'location': 'San Francisco Bay Area',
 'distance': 25,
 'posted_since': '1 week',
 'max_results': 100}

run = client.actor("cheapget/linkedin-job-search").call(run_input=run_input)

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

🟨 JavaScript / Node.js SDK

import { ApifyClient } from 'apify-client';

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

const run = await client.actor('cheapget/linkedin-job-search').call({
  runInput: {
      "search_terms": [
          "Product Manager",
          "Software Engineer"
      ],
      "country": "United States",
      "location": "San Francisco Bay Area",
      "distance": 25,
      "posted_since": "1 week",
      "max_results": 100
  }
});

for await (const item of client.dataset(run.defaultDatasetId).iterateItems()) {
  console.log(item);
}

🌐 REST API (curl)

curl -X POST "https://api.apify.com/v2/acts/cheapget~linkedin-job-search/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"search_terms": ["Product Manager", "Software Engineer"], "country": "United States", "location": "San Francisco Bay Area", "distance": 25, "posted_since": "1 week", "max_results": 100}'

Ⓜ️ Make.com Integration

Get Started with Make.com (1000 Free Credits) 🎁

Step 1: Add the Make.com module "Run an Actor"
Step 2: Enable mapping and paste Actor ID `E4AxzjmyF6mhrmww2`
Step 3: Paste the input JSON and run synchronously
Step 4: Add "Get Dataset Items" with `defaultDatasetId`

🎱 N8N.io Integration

Open Source Workflow Automation ⚡

Step 1: Add the Apify node "Run an Actor and get dataset"
Step 2: Set Selection Mode to By ID
Step 3: Paste Actor ID `E4AxzjmyF6mhrmww2`
Step 4: Add your input JSON and read items from `defaultDatasetId`

📚 API Documentation

• MCP API - Model Context Protocol integration
• Python API - Complete Python client documentation with examples
• JavaScript API - Node.js and browser integration guide

🏗️ Metadata for Developers (JSON-LD)

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "LinkedIn Job Search - Apify Actor",
  "applicationCategory": "DeveloperApplication",
  "applicationSubCategory": "Job Data Extraction",
  "operatingSystem": "Cloud",
  "offers": {
    "@type": "Offer",
    "price": "0.00",
    "priceCurrency": "USD",
    "priceValidUntil": "2099-12-31",
    "availability": "https://schema.org/InStock"
  },
  "description": "Extract comprehensive LinkedIn job listings with 35+ rich data points per posting.",
  "featureList": [
    "Professional Job Intelligence",
    "Comprehensive Job Data",
    "Structured Output Format"
  ],
  "keywords": "linkedin-job-search, structured data extraction, apify actor",
  "author": {
    "@type": "Organization",
    "name": "cheapget",
    "url": "https://apify.com/cheapget?fpr=aiagentapi"
  },
  "softwareVersion": "1.0.0",
  "datePublished": "2024-01-01",
  "dateModified": "2026-04-12"
}

🚀 Performance Tips

Optimize your extraction runs for speed, cost, and data quality with these best practices:

💰 Cost Optimization

• Test First: Start with 1-2 search terms and max_results: 10 to verify output quality before scaling
• Precise Keywords: Use specific job titles (e.g., "Senior Frontend Engineer" vs. "Developer") for more relevant results
• Monitor Usage: Each job costs $0.00296—check the Usage tab in Apify Console for real-time cost tracking

⚡ Speed Optimization

• Batch Processing: Process multiple search terms in a single run to maximize efficiency
• Recent Posts: Use "1 week" or "24 hours" filters for faster results and fresher job data
• Location Specificity: Specify metro areas (e.g., "San Francisco Bay Area") for better targeting and faster searches

🛡️ Data Quality Tips

• Country Names: Use exact country names as they appear in the dropdown (e.g., "United States", not "USA")
• Fresh Data: LinkedIn job postings are highly dynamic—schedule daily or weekly runs for time-sensitive recruiting
• Salary Availability: Salary data varies by region—US jobs (especially CA, CO, NY) have higher disclosure rates due to transparency laws
• Company Verification: All company data comes from verified LinkedIn company pages for authenticity

📊 Best Practices

• Rate Limits: The Actor handles rate limiting automatically with intelligent proxy rotation
• Data Freshness: All metrics are captured at extraction time—engagement and applicant data reflect real-time values
• Remote Filtering: Use is_remote or work_from_home fields to filter remote-friendly positions post-extraction

❓ FAQ

What job data can I extract?

You can extract comprehensive job listings with 35+ fields including job title, description, salary ranges, contact info (emails, phones), social links, required skills, company information (ratings, employee count, revenue), location details, remote work options, and posting dates.

How many jobs can I extract?

There's no hard limit. You can set max_results to any value (minimum 10). Each search term will return up to the specified number of results. For example, 3 search terms with max_results: 100 can return up to 300 jobs.

Which countries are supported?

The Actor supports 95+ countries including United States, United Kingdom, Canada, Australia, Germany, India, France, Singapore, and many more. See the country dropdown in the input schema for the complete list.

How accurate is the salary data?

Salary data comes directly from employer disclosures on LinkedIn. Accuracy is 100% for disclosed salaries. However, not all jobs include salary information—availability varies by country, industry, and local pay transparency laws.

Can I filter by remote jobs only?

The Actor extracts all matching jobs. You can filter results after extraction using the is_remote (boolean) or work_from_home (string: "remote", "hybrid", "on-site") fields in the output data.

What date formats are supported for posted_since?

The Actor supports both relative formats ("1 week", "30 days", "2 months", "1 year") and absolute dates ("2024-12-01", "2025-01-15"). Relative formats are recommended for recurring scheduled runs.

How long does processing take?

Processing time depends on the number of search terms and max_results. Typical benchmarks:

• 1 search term, 10 results: 30-60 seconds
• 3 search terms, 100 results each: 2-4 minutes
• 5 search terms, 200 results each: 5-8 minutes

What output formats are available?

The Actor outputs data in JSON format by default. You can export results to CSV or Excel formats using Apify's dataset export features in the Output tab.

Why are some company fields empty?

Company data availability depends on whether the employer has a complete LinkedIn company page. Verified companies with active LinkedIn presence have the most complete data (ratings, employee counts, revenue, etc.).

🏷️ LinkedIn Job Search

🔥 Search Terms: linkedin scraper, linkedin jobs api, linkedin job listings, linkedin salary data, linkedin data extraction, linkedin job search, linkedin automation, linkedin job aggregator, linkedin job parser, linkedin career data, linkedin talent intelligence, linkedin recruiting api, linkedin hiring data, linkedin company insights, linkedin employee data, linkedin skills extraction, linkedin job feed, professional network scraper, b2b job data, enterprise recruiting data, hr tech api, workforce analytics, employment trends, job market intelligence, talent acquisition, recruitment automation, labor market data, career platform, job board scraper, professional jobs api

💼 Use Case: talent-intelligence recruiting-automation labor-market-analytics competitive-hiring-analysis workforce-planning career-platform hr-tech talent-acquisition skills-gap-analysis salary-benchmarking company-research headcount-planning job-matching-ai recruitment-analytics employment-trends job-aggregation market-intelligence candidate-sourcing employer-branding hiring-insights professional-data career-coaching job-board-integration talent-marketplace workforce-intelligence recruitment-platform job-search-engine career-analytics

⚖️ Legal & Compliance

This actor extracts publicly available data only. It does not bypass authentication, access private content, or violate platform terms of service. You are responsible for:

• Data Rights: Ensuring you have permission to collect and use the extracted data
• Privacy Compliance: Adhering to GDPR, CCPA, and other applicable privacy laws when processing data
• Platform Terms: Respecting the platform's terms of service and usage policies
• Ethical Use: Using extracted data responsibly and in compliance with applicable laws

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Last Updated: April 12, 2026