# LinkedIn Jobs Search Scraper (`neuton/linkedin-jobs-search-scraper`) Actor Search public LinkedIn jobs by keyword and location without login. Export job IDs, titles, companies, locations, posting dates, job URLs, company URLs, logos, and optional full descriptions for recruiting, market maps, and sales triggers. - **URL**: https://apify.com/neuton/linkedin-jobs-search-scraper.md - **Developed by:** [Ashwin Prasad](https://apify.com/neuton) (community) - **Categories:** Jobs - **Stats:** 2 total users, 1 monthly users, 25.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Search Scraper - No Login Scrape public LinkedIn job search results for recruiting, labor-market research, account-based sales triggers, hiring intelligence, and job board enrichment. This actor is the first piece of a LinkedIn scraper family focused on public job data rather than personal profile harvesting. ### Why this actor exists LinkedIn jobs are one of the clearest paid scraping categories on Apify. Buyers search for LinkedIn jobs scraper, LinkedIn job postings API, LinkedIn hiring signals, LinkedIn job export, LinkedIn jobs CSV, and LinkedIn recruiting data every day. This actor starts with the highest-value and lowest-risk LinkedIn surface: public job postings. ### Output Rows include job ID, title, company, location, posted date text, job URL, company URL, logo URL, search keyword, search location, and optionally full job description plus seniority, employment type, job function, and industries. ### Best use cases - Track hiring signals at target accounts for sales and outbound campaigns - Build job-market datasets by keyword, location, company, role, seniority, or industry - Enrich job boards, CRM records, Clay tables, spreadsheets, and BI dashboards - Monitor competitor hiring, expansion signals, remote-job demand, and skill trends - Feed public job-posting rows into AI agents, lead scoring models, and market maps ### Agent and automation workflows Use this actor as a low-cost LinkedIn jobs data API for Apify tasks, webhooks, Make, Zapier, n8n, Clay, Airtable, Google Sheets, CRM enrichment, and AI-agent pipelines. The output is flat JSON/CSV, so agents can filter by title, company, location, seniority, employment type, industry, and job URL without extra parsing. Common automation patterns: - Daily hiring-signal monitor for target accounts - New-role alerts for sales, recruiting, and market-intelligence teams - Job-board enrichment from LinkedIn job IDs and URLs - Labor-market snapshots by keyword and country - AI lead scoring based on newly opened roles ### SEO keywords LinkedIn jobs scraper, LinkedIn job postings API, LinkedIn jobs export, LinkedIn hiring signals, LinkedIn recruiting data, LinkedIn jobs CSV, LinkedIn job search scraper, public LinkedIn jobs scraper, LinkedIn jobs no login scraper. ### Example input ```json {"queries":["AI engineer"],"locations":["United States"],"includeDescriptions":true,"maxResultsPerQuery":50} ```` ### Pricing recommendation Launch at $0.34 per 1,000 basic job rows to sit just below the visible low-price LinkedIn Jobs leaders and win initial traction. Treat full-description jobs as a heavier workflow and price the companion details actor at $0.70 per 1,000 detailed rows. Reprice upward only after repeat users, Store bookmarks, or issue-free production runs appear. ### Responsible use This actor collects public job listing information. Do not use it for spam, unlawful discrimination, or automated abuse of LinkedIn. # Actor input Schema ## `queries` (type: `array`): Job keywords, e.g. software engineer, AI engineer, sales development representative. ## `locations` (type: `array`): Locations such as United States, London, Remote, Bengaluru. ## `geoId` (type: `string`): Optional LinkedIn geoId. Leave empty to use location text. ## `datePosted` (type: `string`): Optional LinkedIn f\_TPR value such as r86400, r604800, r2592000. ## `includeDescriptions` (type: `boolean`): Fetch each job detail page for full description and criteria fields. ## `maxResultsPerQuery` (type: `integer`): Maximum jobs for each keyword/location pair. ## Actor input object example ```json { "queries": [ "software engineer" ], "locations": [ "United States" ], "includeDescriptions": false, "maxResultsPerQuery": 25 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("neuton/linkedin-jobs-search-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("neuton/linkedin-jobs-search-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 '{}' | apify call neuton/linkedin-jobs-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=neuton/linkedin-jobs-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Search Scraper", "description": "Search public LinkedIn jobs by keyword and location without login. Export job IDs, titles, companies, locations, posting dates, job URLs, company URLs, logos, and optional full descriptions for recruiting, market maps, and sales triggers.", "version": "1.0", "x-build-id": "vp2MDa0LTzQzslWTt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/neuton~linkedin-jobs-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-neuton-linkedin-jobs-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/neuton~linkedin-jobs-search-scraper/runs": { "post": { "operationId": "runs-sync-neuton-linkedin-jobs-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/neuton~linkedin-jobs-search-scraper/run-sync": { "post": { "operationId": "run-sync-neuton-linkedin-jobs-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "queries": { "title": "Job keywords", "type": "array", "description": "Job keywords, e.g. software engineer, AI engineer, sales development representative.", "items": { "type": "string" }, "default": [ "software engineer" ] }, "locations": { "title": "Locations", "type": "array", "description": "Locations such as United States, London, Remote, Bengaluru.", "items": { "type": "string" }, "default": [ "United States" ] }, "geoId": { "title": "Geo ID", "type": "string", "description": "Optional LinkedIn geoId. Leave empty to use location text." }, "datePosted": { "title": "Date posted filter", "type": "string", "description": "Optional LinkedIn f_TPR value such as r86400, r604800, r2592000." }, "includeDescriptions": { "title": "Include job descriptions", "type": "boolean", "description": "Fetch each job detail page for full description and criteria fields.", "default": false }, "maxResultsPerQuery": { "title": "Max results per query/location", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum jobs for each keyword/location pair.", "default": 25 } } }, "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 } } } } } } } } } } ```