Linkedin Jobs Scraper

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Every LinkedIn job listing, structured and ready for analysis. Salary ranges, company details, full descriptions, and 22+ fields per job. Search multiple titles across multiple locations in one run. 50 jobs in under 30 seconds.

How it works

👁 How it works

✨ Why use this scraper?

Spending hours scrolling through LinkedIn job boards? Copy-pasting listings into spreadsheets? Missing salary data because LinkedIn hides it after a few views?

• 💰 Salary data when LinkedIn has it. Min, max, and currency extracted from every listing that shows compensation.
• 🔍 Multi-title, multi-location search. Run "Data Engineer" + "ML Engineer" across "New York" + "London" + "Berlin" in one call. All combinations, deduplicated.
• 📝 Full job descriptions. Complete requirements, responsibilities, and qualifications. Not just the preview card.
• 📧 Email extraction. Contact emails found in job descriptions are extracted automatically.
• 🏢 Company details. Name, logo, industry, LinkedIn URL, and direct apply links.
• ⚡ No login required. Works on public LinkedIn data. No cookies, no account, no API key.
• 🔄 Automatic deduplication. Cross-product searches never return the same job twice.

🎯 Use cases

📥 Input parameters

Search

Filters

📊 Output data

{
"id":"4334158613",
"title":"Software Engineer, Fullstack",
"companyName":"Notion",
"companyUrl":"https://www.linkedin.com/company/notionhq",
"companyLogo":"https://media.licdn.com/dms/image/...",
"companyIndustry":"Software Development",
"location":"San Francisco, CA",
"city":"San Francisco",
"state":"CA",
"country":"United States",
"isRemote":false,
"jobUrl":"https://www.linkedin.com/jobs/view/4334158613",
"jobUrlDirect":"https://notion.com/careers/apply/...",
"datePosted":"2026-04-01",
"salaryMin":150000,
"salaryMax":220000,
"salaryCurrency":"USD",
"jobType":["Full-time"],
"jobLevel":"Mid-Senior level",
"jobFunction":"Engineering and Information Technology",
"numApplicants":"Over 100 applicants",
"description":"We are looking for a talented engineer...",
"emails":["careers@notion.com"],
"easyApply":false,
"scrapedAt":"2026-04-09T10:30:00Z"
}

🗂️ Data fields

🚀 Examples

Find remote Python jobs in Europe

{
"titles":["Python Developer","Backend Engineer"],
"locations":["London","Berlin","Amsterdam"],
"workType":"2",
"contractType":"F",
"maxItems":100
}

Track new data science openings this week

{
"titles":["Data Scientist","ML Engineer","Data Analyst"],
"locations":["United States"],
"publishedAt":"r604800",
"maxItems":200
}

Monitor new full-time openings in multiple cities

{
"titles":["Software Engineer","Backend Developer"],
"locations":["San Francisco","New York","Austin"],
"contractType":"F",
"publishedAt":"r86400",
"maxItems":100
}

Entry-level jobs with Easy Apply

{
"titles":["Junior Developer","Graduate Engineer"],
"locations":["New York","Chicago"],
"experienceLevel":"2",
"easyApply":true,
"maxItems":50
}

💻 Integrations

Python

from apify_client import ApifyClient
client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("silentflow/linkedin-jobs-scraper").call(run_input={
"titles":["Software Engineer","Backend Developer"],
"locations":["New York","San Francisco"],
"contractType":"F",
"publishedAt":"r604800",
"maxItems":100,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
 salary =f"${item['salaryMin']:,}-${item['salaryMax']:,}"if item.get("salaryMin")else"N/A"
print(f"{item['title']} @ {item['companyName']} | {salary} | {item['location']}")

JavaScript

import{ ApifyClient }from'apify-client';
const client =newApifyClient({token:'YOUR_API_TOKEN'});
const run =await client.actor('silentflow/linkedin-jobs-scraper').call({
titles:['Software Engineer','Backend Developer'],
locations:['New York','San Francisco'],
contractType:'F',
publishedAt:'r604800',
maxItems:100,
});
const{ items }=await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item=>{
const salary = item.salaryMin ?`$${item.salaryMin.toLocaleString()}-$${item.salaryMax.toLocaleString()}`:'N/A';
 console.log(`${item.title} @ ${item.companyName} | ${salary} | ${item.location}`);
});

📈 Performance

💡 Tips for best results

1. Use multiple titles to cover job title variations: ["Python Developer", "Backend Engineer", "Software Developer"]
2. Cross titles with locations to search multiple markets in one run: 3 titles x 3 cities = 9 searches
3. Filter by date with publishedAt: "r604800" to get only jobs from the past week
4. Start with 25 results to test your filters before scaling
5. Combine filters for precision: fulltime + remote + past week gives you only the freshest relevant listings

❓ FAQ

Q: Do I need a LinkedIn account? No. The scraper uses public LinkedIn job listings. No login, no cookies, no API key.

Q: Why are some jobs missing salary data? LinkedIn only shows salary for a subset of listings. The scraper extracts it when present.

Q: What does the titles x locations cross-product mean? If you provide 2 titles and 3 locations, the scraper runs 6 searches (every title in every location) and deduplicates results.

Q: What's the difference between jobUrl and jobUrlDirect? jobUrl is the LinkedIn listing page. jobUrlDirect is the external apply link (when available).

Q: How many jobs can I get per run? LinkedIn returns up to ~1,000 results per title/location combination. Use multiple titles and locations to get more: 3 titles x 3 cities can yield thousands of results in one run.

Q: Can I search globally? Yes. Set locations to ["United States", "United Kingdom", "Germany"] or any combination. LinkedIn handles location resolution.

📬 Support

Need something this scraper doesn't do yet? We ship features fast.

• Feature requests go straight to our backlog
• Enterprise needs? We do custom integrations
• Response time: usually under 24 hours

Check out our other scrapers: SilentFlow on Apify