# LinkedIn Jobs Scraper (`sourabhbgp/linkedin-jobs-scraper`) Actor Scrape LinkedIn job listings with salary, description, and 7+ filters. HTTP-only, no cookies needed. Search by keyword + location or get full details. - **URL**: https://apify.com/sourabhbgp/linkedin-jobs-scraper.md - **Developed by:** [Sourabh Kumar](https://apify.com/sourabhbgp) (community) - **Categories:** Jobs, Lead generation - **Stats:** 4 total users, 2 monthly users, 75.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 results 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 — 100 Free | Salary, Description, Filters Scrape LinkedIn job listings at scale without login or cookies. Fast HTTP-only scraper — no browser needed, no headless Chrome overhead. 2 modes: search by keywords/location with filters, or get full details for specific job URLs. **$2 per 1,000 results** — extracts salary, description, seniority level, and applicant count that most competitors miss. 100 free results lifetime, no credit card needed. ### :mag: What Does It Do? This actor scrapes LinkedIn job data using two modes plus an enrichment option: 1. **Search** — Find jobs by keywords and location with filters (date posted, job type, experience level, workplace type, company, sort order) 2. **Detail** — Get full details for specific job URLs or IDs (description, salary, seniority, employment type, industries, apply link) 3. **Search + Enrich** — Search for jobs, then automatically fetch full details for each result (combines both modes) HTTP-only architecture means it runs 5-10x faster and costs 90% less than browser-based scrapers. No Playwright, no Puppeteer, no login required. Uses LinkedIn's public guest API endpoints. ### :bar_chart: What Data Can You Extract? #### Search Results | Field | Type | Description | |-------|------|-------------| | `jobId` | string | LinkedIn job ID | | `title` | string | Job title | | `company` | string | Company name | | `companyUrl` | string | Company LinkedIn page URL | | `companyLogo` | string | Company logo image URL | | `location` | string | Job location | | `postedDate` | string | When the job was posted | | `jobUrl` | string | Full LinkedIn job URL | | `isActivelyHiring` | boolean | Whether company is actively hiring | | `scrapedAt` | string | ISO timestamp of when data was scraped | #### Detail Results (enrichDetails or detail mode) All search result fields plus: | Field | Type | Description | |-------|------|-------------| | `salary` | string | Salary range (when available) | | `applicantCount` | string | Number of applicants | | `description` | string | Full job description (plain text) | | `seniorityLevel` | string | Entry level, Mid-Senior, Director, etc. | | `employmentType` | string | Full-time, Part-time, Contract, etc. | | `jobFunction` | string | Engineering, Marketing, Sales, etc. | | `industries` | string | Industry categories | | `applyUrl` | string | Direct application link | ### :bulb: Use Cases - **Job market research** — analyze hiring trends by role, location, and industry at scale - **Salary benchmarking** — collect salary data across companies and regions to benchmark compensation - **Competitor hiring analysis** — track what roles competitors are hiring for and how aggressively - **Recruitment pipeline** — build candidate sourcing lists from job postings and company data - **Lead generation from job postings** — identify companies actively hiring as potential sales leads - **HR analytics** — aggregate job data to understand workforce demand, skill requirements, and market gaps ### :moneybag: How Much Does It Cost? - **100 free results** — lifetime, no credit card needed - After that: **$2 per 1,000 results** ($0.002 each) | Results | Cost | |---------|------| | 100 | Free | | 50 enriched jobs | $0.10 | | 1,000 | $2 | | 10,000 | $20 | | 100,000 | $200 | Competitors charge $5-8 per 1,000 results. This actor is **3-4x cheaper** with salary and description data included. ### :inbox_tray: Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `mode` | enum | `search` | `search` or `detail` | | `keywords` | string | `software engineer` | Job search keywords (search mode) | | `location` | string | `United States` | Location to search (search mode) | | `urls` | string[] | `[]` | LinkedIn job URLs or IDs (detail mode) | | `maxResults` | integer | `50` | Max results to return (0 = unlimited, max 1000) | | `enrichDetails` | boolean | `false` | Fetch full details for each search result | | `datePosted` | enum | `any` | `any`, `past24h`, `pastWeek`, `pastMonth` | | `jobType` | enum | `any` | `any`, `full-time`, `part-time`, `contract`, `temporary`, `volunteer`, `internship` | | `experienceLevel` | enum | `any` | `any`, `internship`, `entry`, `associate`, `mid-senior`, `director`, `executive` | | `workplaceType` | enum | `any` | `any`, `on-site`, `remote`, `hybrid` | | `companyId` | string | — | LinkedIn company ID to filter by specific company | | `sortBy` | enum | `relevant` | `relevant` or `recent` | #### Search by Keywords ```json { "mode": "search", "keywords": "data engineer", "location": "San Francisco, CA", "maxResults": 100, "datePosted": "pastWeek", "jobType": "full-time", "workplaceType": "remote" } ```` #### Get Job Details ```json { "mode": "detail", "urls": [ "https://www.linkedin.com/jobs/view/4370317193", "https://www.linkedin.com/jobs/view/software-engineer-at-google-4370317193", "4370317193" ] } ``` #### Search + Enrich with Full Details ```json { "mode": "search", "keywords": "product manager", "location": "New York", "maxResults": 50, "enrichDetails": true, "experienceLevel": "mid-senior", "sortBy": "recent" } ``` ### :outbox\_tray: Output Example #### Detail Result (enriched or detail mode) ```json { "jobId": "4370317193", "title": "Senior Software Engineer", "company": "Google", "companyUrl": "https://www.linkedin.com/company/google", "companyLogo": "https://media.licdn.com/dms/image/v2/C4D0BAQ...", "location": "Mountain View, CA", "postedDate": "2 weeks ago", "jobUrl": "https://www.linkedin.com/jobs/view/4370317193", "isActivelyHiring": true, "salary": "$170,000/yr - $250,000/yr", "applicantCount": "Over 200 applicants", "description": "We are looking for a Senior Software Engineer to join our Cloud Platform team. You will design and build large-scale distributed systems, mentor junior engineers, and drive technical decisions across the organization. Requirements: 5+ years of experience in backend development, proficiency in Java, Go, or C++, experience with distributed systems and cloud infrastructure...", "seniorityLevel": "Mid-Senior level", "employmentType": "Full-time", "jobFunction": "Engineering and Information Technology", "industries": "Technology, Information and Internet", "applyUrl": "https://careers.google.com/jobs/results/123456", "scrapedAt": "2026-04-07T12:00:00.000Z" } ``` ### :zap: Tips - **Use `enrichDetails: true`** to get salary, description, and seniority data — search results alone only include title, company, and location - **Combine filters** for targeted results — e.g., `remote` + `full-time` + `past24h` to find fresh remote full-time jobs - **LinkedIn caps results at ~1,000** per search query — use more specific keywords or filters to stay within this limit - **Use `companyId`** to get all jobs from a specific company — find the company ID from their LinkedIn URL (it's the numeric ID in the page source) - **Sort by `recent`** to get the freshest postings first — useful for monitoring new job openings - **Any LinkedIn job URL format works** — full URLs, slug URLs, bare numeric IDs, and URLs with tracking parameters are all accepted - **Salary data availability varies** — not all job postings include salary; enrichment extracts it when LinkedIn displays it on the detail page - **Rate limiting is handled automatically** — the actor includes retry logic with exponential backoff for 429 responses ### 🔗 Other LinkedIn scrapers - [LinkedIn Profile Scraper](https://apify.com/sourabhbgp/linkedin-profile-scraper) — extract name, headline, experience, education, and articles from public profiles - [LinkedIn Ad Library Scraper](https://apify.com/sourabhbgp/linkedin-ad-library-scraper) — search LinkedIn's Ad Library by keyword, company, or payer - [LinkedIn Company Scraper](https://apify.com/sourabhbgp/linkedin-company-scraper) — extract company data, employee counts, posts, similar companies, and full addresses # Actor input Schema ## `mode` (type: `string`): Scraping mode: search for jobs by keywords/location, or get details for specific job URLs. ## `keywords` (type: `string`): Job search keywords (used in search mode). Example: software engineer, data analyst, product manager. ## `location` (type: `string`): Location to search for jobs (used in search mode). Example: United States, London, Remote. ## `urls` (type: `array`): LinkedIn job URLs or job IDs for detail mode. Also accepts bare job IDs. ## `maxResults` (type: `integer`): Maximum number of results to return. 0 = unlimited (max 1000). ## `enrichDetails` (type: `boolean`): In search mode, fetch full job details for each result (slower but more data). ## `datePosted` (type: `string`): Filter jobs by when they were posted. ## `jobType` (type: `string`): Filter by job type. ## `experienceLevel` (type: `string`): Filter by experience level. ## `workplaceType` (type: `string`): Filter by workplace type. ## `companyId` (type: `string`): LinkedIn company ID to filter jobs by a specific company. ## `sortBy` (type: `string`): Sort search results by relevance or date. ## Actor input object example ```json { "mode": "search", "keywords": "software engineer", "location": "United States", "maxResults": 50, "enrichDetails": false, "datePosted": "any", "jobType": "any", "experienceLevel": "any", "workplaceType": "any", "sortBy": "relevant" } ``` # Actor output Schema ## `datasetItems` (type: `string`): Scraped LinkedIn job listings and details # 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", "location": "United States" }; // Run the Actor and wait for it to finish const run = await client.actor("sourabhbgp/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", "location": "United States", } # Run the Actor and wait for it to finish run = client.actor("sourabhbgp/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", "location": "United States" }' | apify call sourabhbgp/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sourabhbgp/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper", "description": "Scrape LinkedIn job listings with salary, description, and 7+ filters. HTTP-only, no cookies needed. Search by keyword + location or get full details.", "version": "0.0", "x-build-id": "ru01NDO6IhjddF1O6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sourabhbgp~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sourabhbgp-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sourabhbgp~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-sourabhbgp-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sourabhbgp~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-sourabhbgp-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "mode": { "title": "Mode", "enum": [ "search", "detail" ], "type": "string", "description": "Scraping mode: search for jobs by keywords/location, or get details for specific job URLs.", "default": "search" }, "keywords": { "title": "Keywords", "type": "string", "description": "Job search keywords (used in search mode). Example: software engineer, data analyst, product manager." }, "location": { "title": "Location", "type": "string", "description": "Location to search for jobs (used in search mode). Example: United States, London, Remote." }, "urls": { "title": "Job URLs", "type": "array", "description": "LinkedIn job URLs or job IDs for detail mode. Also accepts bare job IDs.", "items": { "type": "string" } }, "maxResults": { "title": "Max Results", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Maximum number of results to return. 0 = unlimited (max 1000).", "default": 50 }, "enrichDetails": { "title": "Enrich with Details", "type": "boolean", "description": "In search mode, fetch full job details for each result (slower but more data).", "default": false }, "datePosted": { "title": "Date Posted", "enum": [ "any", "past24h", "pastWeek", "pastMonth" ], "type": "string", "description": "Filter jobs by when they were posted.", "default": "any" }, "jobType": { "title": "Job Type", "enum": [ "any", "full-time", "part-time", "contract", "temporary", "volunteer", "internship" ], "type": "string", "description": "Filter by job type.", "default": "any" }, "experienceLevel": { "title": "Experience Level", "enum": [ "any", "internship", "entry", "associate", "mid-senior", "director", "executive" ], "type": "string", "description": "Filter by experience level.", "default": "any" }, "workplaceType": { "title": "Workplace Type", "enum": [ "any", "on-site", "remote", "hybrid" ], "type": "string", "description": "Filter by workplace type.", "default": "any" }, "companyId": { "title": "Company ID", "type": "string", "description": "LinkedIn company ID to filter jobs by a specific company." }, "sortBy": { "title": "Sort By", "enum": [ "relevant", "recent" ], "type": "string", "description": "Sort search results by relevance or date.", "default": "relevant" } } }, "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 } } } } } } } } } } ```