💰 LinkedIn Salary Scraper — Job Pay Ranges & Compensation

What does LinkedIn Salary Scraper do?

LinkedIn Salary Scraper extracts pay ranges and compensation from linkedin.com job postings — salary min/max, currency, and period, plus seniority, skills, full descriptions, and employer details — with no login or cookies, at $0.27 per 1,000 results. Salary is read from each posting's detail page, which is enabled by default. Coverage is highest on US roles, where pay-transparency laws require a posted range (~70% of US postings carry one).

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Key features

• 💰 Salary & pay ranges — extracts salaryMin, salaryMax, salaryCurrency, and salaryPeriod from each posting's detail page (detail enrichment is on by default — it's where the salary comes from). Highest fill on US roles thanks to pay-transparency laws; postings without a listed range are still returned with the salary fields null.
• 🎯 Structured filters — or paste a URL — no URL-building required: set keywords, location, and dropdown filters (date, work type, experience, job type, salary, Easy Apply). Or paste a LinkedIn search URL to reuse its filters — or a single job URL (.../jobs/view/123…) to pull one posting directly.
• 🏢 Company pay ranges — paste a LinkedIn company-page URL (or numeric companyId) into companies and get the compensation for every role that employer is hiring for — no ID lookup needed.
• 📈 Seniority, skills & role metadata — each enriched record includes seniority level, employment type, job function, industry, applicant count, and extracted skills alongside the pay range.
• 🌎 Multi-region with presets — pass regions: ["US", "GB", "DE"] for a custom country mix, or pick presets like "nordic", "dach", "anglosphere" (salary fill varies by market — US is richest).
• ⚡ Easy-Apply filter — easyApply: true returns only LinkedIn Easy-Apply postings.
• 🚫 Recruiter-spam filter — removeAgency: true drops third-party staffing-agency listings, keeping direct-employer pay data.
• 🤖 AI-ready output — deterministic aiSummary and skills fields give compact context for comp-analytics agents and MCP workflows.
• 🔔 Notifications — Telegram, Slack, Discord, WhatsApp Cloud API, and generic webhook outputs.
• 📦 Compact mode — core-fields-only payloads for piping salary data straight into dashboards, spreadsheets, or LLM context.

What data can you extract from linkedin.com?

Each result includes Core job fields (scrapedAt, portalUrl, jobId, linkedinJobId, jobUrl, title, location, and country, and more), extended fields (description, descriptionHtml, descriptionMarkdown, and postingBenefits), apply information (applyUrl, applyType, and easyApply), and company metadata (company, companyUrl, companyId, and companyLogo). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Input

The main inputs are an optional location filter and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• keywords — Job search keywords (e.g. "software engineer", "nurse"). Leave blank to browse all jobs in the selected location.
• location — City, state, region, or country (e.g. "Berlin, Germany", "United States"). LinkedIn resolves it for you — no IDs needed. Leave empty for global results.
• geoIds — Advanced/optional override — most users just use 📍 Location, which LinkedIn resolves automatically. For exact targeting, paste a LinkedIn jobs search URL into 🔗 Start URLs (the geoId is read for you), or copy the numeric geoId from a LinkedIn jobs URL. Each geoId becomes a separate query, deduped on jobId. (default: [])
• regions — Two-letter country codes (e.g. "US", "GB", "DE"). Resolved to LinkedIn country geoIds. Use geoIds[] for unsupported markets. (default: [])
• regionPresets — Pre-defined country grouping. Combined with regions[] if both are set.
• datePosted — Filter by posting recency. "lastHour" is unique to this scraper. (default: "anytime")
• jobType — Multi-select employment type filter. (default: [])
• experienceLevel — Multi-select seniority filter. (default: [])
• workType — Multi-select onsite/remote/hybrid filter. (default: [])
• salaryMin — Minimum annual salary (USD). Mapped to LinkedIn's nearest f_SB2 bucket. Post-filtered exactly.
• salaryMax — Maximum annual salary. Post-filtered (LinkedIn has no native max filter).
• salaryIncludeUnknown — When salaryMin/Max set, include jobs with no salary data. (default: true)
• ...and 40 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "keywords": "software engineer",
  "maxResults": 50
}

Filtered search — Narrow results with advanced filters — only matching jobs are returned.

→ Same field set as basic search; fewer, more relevant rows.

{
  "keywords": "software engineer",
  "jobType": [
    "fulltime"
  ],
  "workType": [
    "onsite"
  ],
  "experienceLevel": [
    "internship"
  ],
  "maxResults": 100
}

Incremental tracking — Only emit jobs that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
  "keywords": "software engineer",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "software-engineer-tracker"
}

Compact filtered output — Combine filters with compact mode for a lightweight AI-agent or MCP data source.

→ Core fields only — ideal for piping into LLMs or downstream tools without token overhead.

{
  "keywords": "software engineer",
  "jobType": [
    "fulltime"
  ],
  "workType": [
    "onsite"
  ],
  "maxResults": 50,
  "compact": true
}

Output

Each run produces a dataset of structured job records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example job record

{
  "scrapedAt": "2026-04-27T19:02:37.769Z",
  "portalUrl": "https://www.linkedin.com",
  "source": "linkedin",
  "jobId": "1705fc4ee704bf3584cf2654b20e8f95383167563ff7ecd0184b58d2c7d66236",
  "linkedinJobId": "4406118990",
  "jobUrl": "https://www.linkedin.com/jobs/view/software-engineer-new-grad-at-notion-4406118990",
  "title": "Software Engineer, New Grad",
  "company": "Notion",
  "companyUrl": "https://www.linkedin.com/company/notionhq",
  "companyId": "notionhq",
  "location": "San Francisco, CA",
  "country": "CA",
  "postedAt": "2026-04-24T00:00:00.000Z",
  "applyUrl": "https://www.linkedin.com/jobs/view/software-engineer-new-grad-at-notion-4406118990",
  "applyType": "unknown",
  "description": "About Us Notion helps you build beautiful tools for your life’s work. In today's world of endless apps and tabs, Notion provides one place for teams to get everything done, seamlessly connecting docs,...",
  "descriptionHtml": "<strong>About Us<br><br></strong>Notion helps you build beautiful tools for your life’s work. In today's world of endless apps and tabs, Notion provides one place for teams to get everything done, sea...",
  "descriptionMarkdown": "About Us Notion helps you build beautiful tools for your life’s work. In today's world of endless apps and tabs, Notion provides one place for teams to get everything done, seamlessly connecting docs,...",
  "seniorityLevel": "Not Applicable",
  "employmentType": "Full-time",
  "industry": "Software Development",
  "jobFunction": "Engineering and Information Technology",
  "applicantCount": 200,
  "easyApply": false,
  "contentHash": "eda6ea0d0ad7711b94b796376d0ace88eaf03a62afdefe9708fa4f1c7ae4ae8f",
  "isPromoted": false,
  "postingBenefits": [
    "Actively Hiring"
  ],
  "trackingId": "U6ZtuvNYKrwizG8bYR1Kqw=="
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED. Default output covers NEW / UPDATED / REAPPEARED; set emitUnchanged: true or emitExpired: true to opt into the others.
• firstSeenAt, lastSeenAt — ISO-8601 timestamps tracking the listing across runs.
• isRepost, repostOfId, repostDetectedAt — populated when a new listing matches the tracked content of a previously expired one. Set skipReposts: true to drop detected reposts from the output.

How to scrape linkedin.com

1. Go to LinkedIn Salary Scraper in Apify Console.
2. Configure the input and optional location filter.
3. Set maxResults to control how many results you need.
4. Click Start and wait for the run to finish.
5. Export the dataset as JSON, CSV, or Excel.

Use cases

• Benchmark salaries for a role across a market or city.
• Build compensation datasets for HR, comp, and pay-equity analytics.
• Track pay ranges by company, seniority, and location over time.
• Pull every pay range at a specific employer by pasting its company URL.
• Research pay-transparency compliance and prevailing market rates.
• Feed structured salary data into dashboards, warehouses, or AI agents using compact mode.

How much does it cost to scrape linkedin.com?

LinkedIn Salary Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.0005 per run
• Per result: $0.00027 per job record

Example costs:

• 10 results: $0.0032
• 25 results: $0.00725
• 100 results: $0.028
• 200 results: $0.054
• 500 results: $0.14

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of jobs that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 results per run, daily polling (30 runs/month). Event-pricing examples scale linearly with result count.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.07	$0.00387	$0.06 (94%)	$0.12
15% — moderate broad query	$0.07	$0.01	$0.06 (84%)	$0.32
30% — high-volume aggregator	$0.07	$0.02	$0.05 (69%)	$0.62

Full re-scrape monthly cost at daily polling: $2.04. First month with incremental costs $0.18 / $0.38 / $0.67 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage is included in the per-result fee shown above.

FAQ

How many results can I get from linkedin.com?

The number of results depends on the search query and available jobs on linkedin.com. Use the maxResults parameter to control how many results are returned per run.

Does LinkedIn Salary Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed jobs on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate LinkedIn Salary Scraper with other apps?

Yes. LinkedIn Salary Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use LinkedIn Salary Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use LinkedIn Salary Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode, descriptionMaxLength, a single descriptionFormat, and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape linkedin.com?

This actor extracts publicly available data from linkedin.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

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1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.