# LinkedIn Jobs Scraper - Listings, Salaries & Skills (`fascinating_lentil/linkedin-jobs-scraper`) Actor Scrape LinkedIn Jobs for listings by keyword, location, and filters. Extracts job title, company, description, salary, skills, workplace type, experience level, and more. No login required. - **URL**: https://apify.com/fascinating\_lentil/linkedin-jobs-scraper.md - **Developed by:** [Md Jakaria Mirza](https://apify.com/fascinating_lentil) (community) - **Categories:** Jobs, Lead generation, Social media - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 job scrapeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## LinkedIn Jobs Scraper — Extract Job Listings at Scale ### Description Scrape LinkedIn job listings at scale and turn them into a clean, deduplicated dataset — job titles, companies, salaries, required skills, workplace type, experience level, applicant counts, and more. This LinkedIn Jobs scraper runs from simple keywords and locations with no login and no API key. Export to JSON, CSV, Excel, or HTML, or pull via the Apify API. Scrape LinkedIn Jobs at scale with this production-ready Apify Actor. Supply job keywords and locations, and the Actor extracts comprehensive job listing data including titles, companies, descriptions, salaries, required skills, workplace type, job type, experience level, applicant counts, and more. Built on Crawlee's PlaywrightCrawler with residential proxy rotation, session pool management, and intelligent anti-bot countermeasures, this Actor reliably scrapes LinkedIn without getting blocked. Each search supports pagination up to 500 job listings, and multiple keyword + location combinations can be run in a single execution. Deduplication by LinkedIn job ID ensures clean, unique results. A pay-per-event model charges $0.0015 per successfully scraped job, making it affordable at any scale. #### Use Cases 1. **HR & Recruitment Research** — Benchmark job titles, salaries, required skills, and workplace trends across markets and industries to inform hiring strategy. 2. **Job Aggregators** — Feed LinkedIn job data into your own job board, comparison site, or analytics dashboard with structured, deduplicated records. 3. **Salary Benchmarking** — Collect salary ranges from job listings to analyse compensation trends by role, location, experience level, and company size. 4. **Talent Mapping** — Identify which companies are hiring for specific roles in specific regions, and map competitor hiring activity over time. 5. **Competitor Hiring Analysis** — Monitor what skills, seniority levels, and locations your competitors are hiring for, and detect strategic shifts. ### Sample Output ```json { "jobTitle": "Software Engineer", "companyName": "Google", "companyLinkedInUrl": "https://www.linkedin.com/company/google/", "jobLocation": "London, England", "workplaceType": "Hybrid", "jobType": "Full-time", "experienceLevel": "Mid-Senior level", "postedDate": "Posted 2 weeks ago", "numberOfApplicants": 47, "jobDescription": "Google's software engineers develop the next-generation technologies...", "requiredSkills": ["JavaScript", "Python", "Go", "Distributed Systems"], "salaryRange": "$120,000 - $180,000", "applyUrl": "https://www.linkedin.com/jobs/view/123456789/", "jobId": "123456789", "companySize": "10,001+ employees", "companyIndustry": "Technology, Information and Internet", "easyApply": true, "keywordUsed": "software engineer", "locationUsed": "London, UK", "scrapedAt": "2026-06-08T13:30:00.000Z" } ```` ### Pricing | Unit | Price | |-------------|----------| | Per job | $0.0015 | Charges are incurred only when a job record is successfully scraped and pushed to the dataset. There are no upfront fees, no monthly minimums, and no hidden costs. ### Input Parameters | Parameter | Type | Required | Default | Description | |--------------------|------------|----------|----------------|--------------------------------------------------| | keywords | string\[] | Yes | — | Job titles, skills, or search terms | | locations | string\[] | Yes | — | City, region, or country | | workplaceType | string\[] | No | \[] | Filter: remote, hybrid, onsite | | jobType | string\[] | No | \[] | Filter: full-time, part-time, contract, etc. | | experienceLevel | string\[] | No | \[] | Filter: entry, mid-senior, director, etc. | | maxResults | number | No | 50 | Maximum total jobs across all searches | | maxJobsPerSearch | number | No | 25 | Max jobs per keyword + location combination | | proxyConfiguration | object | No | RESIDENTIAL | Apify proxy settings | ### How to Scrape LinkedIn Jobs (Step by Step) 1. Click **Try for free** / **Run**. 2. Enter your job **keywords** (titles or skills) and **locations** (city, region, or country). 3. Optionally filter by workplace type, job type, and experience level. 4. Set **Max Results** and **Max Jobs Per Search** — start small to test. 5. Run, then export results as JSON, CSV, Excel, or HTML, or pull them via the Apify API. ### Technical Details - **Runtime:** Node.js 20 + Playwright (Chrome) - **SDK:** Apify SDK v3 + Crawlee v3 - **Proxy:** Residential proxy rotation (required for LinkedIn) - **Anti-bot:** Session pool (max 20 uses), random delays (1500-4000ms), 3 retries with retryOnBlocked, cookie acceptance - **Pagination:** Infinite scroll with automatic load-more detection - **Deduplication:** By LinkedIn job ID across all searches # Actor input Schema ## `keywords` (type: `array`): Job titles, skills, or search terms to query ## `locations` (type: `array`): City, region, or country for job search ## `workplaceType` (type: `array`): Filter by workplace type ## `jobType` (type: `array`): Filter by employment type ## `experienceLevel` (type: `array`): Filter by seniority level ## `maxResults` (type: `integer`): Maximum number of jobs to scrape across all searches ## `maxJobsPerSearch` (type: `integer`): Maximum jobs to scrape per keyword + location combination ## `proxyConfiguration` (type: `object`): Apify proxy settings (residential recommended for LinkedIn) ## Actor input object example ```json { "keywords": [ "software engineer" ], "locations": [ "London, UK" ], "workplaceType": [], "jobType": [], "experienceLevel": [], "maxResults": 50, "maxJobsPerSearch": 25, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "software engineer" ], "locations": [ "London, UK" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fascinating_lentil/linkedin-jobs-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "keywords": ["software engineer"], "locations": ["London, UK"], } # Run the Actor and wait for it to finish run = client.actor("fascinating_lentil/linkedin-jobs-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "software engineer" ], "locations": [ "London, UK" ] }' | apify call fascinating_lentil/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fascinating_lentil/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper - Listings, Salaries & Skills", "description": "Scrape LinkedIn Jobs for listings by keyword, location, and filters. Extracts job title, company, description, salary, skills, workplace type, experience level, and more. No login required.", "version": "1.0", "x-build-id": "E1A1ofU1NcpcMIyUf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fascinating_lentil~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fascinating_lentil-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fascinating_lentil~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-fascinating_lentil-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fascinating_lentil~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-fascinating_lentil-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "keywords", "locations" ], "properties": { "keywords": { "title": "Job Keywords", "type": "array", "description": "Job titles, skills, or search terms to query", "default": [ "software engineer" ], "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "City, region, or country for job search", "default": [ "London, UK" ], "items": { "type": "string" } }, "workplaceType": { "title": "Workplace Type", "type": "array", "description": "Filter by workplace type", "items": { "type": "string", "enum": [ "remote", "hybrid", "onsite" ] }, "default": [] }, "jobType": { "title": "Job Type", "type": "array", "description": "Filter by employment type", "items": { "type": "string", "enum": [ "full-time", "part-time", "contract", "internship", "temporary", "volunteer" ] }, "default": [] }, "experienceLevel": { "title": "Experience Level", "type": "array", "description": "Filter by seniority level", "items": { "type": "string", "enum": [ "internship", "entry", "associate", "mid-senior", "director", "executive" ] }, "default": [] }, "maxResults": { "title": "Max Results (total)", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of jobs to scrape across all searches", "default": 50 }, "maxJobsPerSearch": { "title": "Max Jobs Per Search", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum jobs to scrape per keyword + location combination", "default": 25 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Apify proxy settings (residential recommended for LinkedIn)", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```