# Linkedin Jobs Scraper (`cute_scheme/linkedin-jobs-scraper`) Actor Extract structured job listings from LinkedIn at scale — titles, salaries, tech stacks, seniority levels, company data, and full descriptions. AI-parsed fields, proxy-ready, and built for production pipelines. - **URL**: https://apify.com/cute\_scheme/linkedin-jobs-scraper.md - **Developed by:** [Sarfraz Mohammed](https://apify.com/cute_scheme) (community) - **Categories:** Automation, Jobs - **Stats:** 3 total users, 1 monthly users, 88.2% runs succeeded, NaN 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.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 — Apify Actor Scrapes job listings from LinkedIn's public job board. No login required. ### Features - 🔍 Search by **keywords** and **location** - 🗓️ Filter by **date posted** (24h / week / month) - 💼 Filter by **job type** (full-time, part-time, contract, internship) - 📈 Filter by **experience level** (entry, mid-senior, director, etc.) - 🏠 Filter by **workplace type** (on-site, remote, hybrid) - 📄 Optional **full job description** scraping (visits each job page) - 🔄 **Pagination** — scrapes up to 500 jobs per run - 🌐 **Proxy support** via Apify Proxy ### Output Fields Each job record contains: | Field | Description | |-------|-------------| | `jobId` | LinkedIn job ID | | `jobUrl` | Direct link to the job posting | | `title` | Job title | | `company` | Company name | | `location` | Job location | | `postedAt` | ISO date when posted | | `postedText` | Human-readable "X days ago" | | `salary` | Salary range (if displayed) | | `isPromoted` | Whether it's a sponsored listing | | `description` | Full job description (if `scrapeJobDetails: true`) | | `seniorityLevel` | Seniority level from job criteria | | `employmentType` | Employment type from job criteria | | `jobFunction` | Job function category | | `industries` | Industry categories | | `applicantCount` | Number of applicants (if shown) | | `skills` | Required skills listed | | `scrapedAt` | ISO timestamp of when scraped | | `searchKeywords` | The keywords used in the search | | `searchLocation` | The location used in the search | ### Input Example ```json { "searchKeywords": "Software Engineer", "location": "Toronto, ON", "maxJobs": 100, "datePosted": "past_week", "jobType": "full_time", "experienceLevel": "mid_senior", "workplaceType": "remote", "scrapeJobDetails": true, "proxy": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ```` ### Usage Notes - **Proxy**: Residential proxies are strongly recommended. LinkedIn aggressively blocks datacenter IPs. - **Rate limiting**: The actor uses randomised delays between requests to mimic human behavior. - **Login not required**: Only public job listings are scraped (no personal data). - **Concurrency**: Set to 1 intentionally — LinkedIn detects parallel requests. ### Local Development ```bash ## Install dependencies npm install ## Run locally (uses Apify local storage) npx apify run ## Or with direct node APIFY_IS_AT_HOME=0 node src/main.js ``` ### Deployment to Apify ```bash ## Login to Apify CLI npx apify login ## Deploy npx apify push ``` ### Extending This Actor This actor is designed as a starting point. Common extensions: - **Company details**: Cross-reference the company LinkedIn page - **Deduplication**: Use `jobId` as the key in a named dataset - **Notifications**: Pipe results to Slack/email via Apify integrations - **Scheduling**: Set up a daily schedule in Apify console - **Storage**: Export to Google Sheets or your database via webhooks # Actor input Schema ## `startUrls` (type: `array`): Optional. Provide direct LinkedIn Jobs search URLs to scrape instead of building from keyword inputs. Useful for saved searches or advanced filters. ## `keywords` (type: `array`): One or more job titles or keywords. Each entry runs as a separate search. Results from all keywords go into the same dataset. ## `searchKeywords` (type: `array`): Alias for Keywords — accepts the same values. If both are provided they are merged. ## `location` (type: `string`): Job location (e.g. 'Toronto, ON', 'Remote', 'United States') ## `maxJobs` (type: `integer`): Maximum number of job listings to scrape ## `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 ## `scrapeJobDetails` (type: `boolean`): If enabled, visits each job page to get full description. Slower but more complete. ## `proxy` (type: `object`): Proxy settings for the scraper ## Actor input object example ```json { "startUrls": [ { "url": "https://www.linkedin.com/jobs/search/?keywords=Software%20Engineer&location=Toronto" } ], "keywords": [ "Salesforce Architect", "Data Engineer", "Technical Program Manager" ], "searchKeywords": [ "Software Engineer" ], "location": "Toronto, ON", "maxJobs": 50, "datePosted": "any", "jobType": "any", "experienceLevel": "any", "workplaceType": "any", "scrapeJobDetails": true } ``` # 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("cute_scheme/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 = {} # Run the Actor and wait for it to finish run = client.actor("cute_scheme/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 '{}' | apify call cute_scheme/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=cute_scheme/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Jobs Scraper", "description": "Extract structured job listings from LinkedIn at scale — titles, salaries, tech stacks, seniority levels, company data, and full descriptions. AI-parsed fields, proxy-ready, and built for production pipelines.", "version": "1.0", "x-build-id": "OZWEqeRaurjMQOzPH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/cute_scheme~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-cute_scheme-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/cute_scheme~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-cute_scheme-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/cute_scheme~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-cute_scheme-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": { "startUrls": { "title": "Start URLs", "type": "array", "description": "Optional. Provide direct LinkedIn Jobs search URLs to scrape instead of building from keyword inputs. Useful for saved searches or advanced filters.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "keywords": { "title": "Keywords", "type": "array", "description": "One or more job titles or keywords. Each entry runs as a separate search. Results from all keywords go into the same dataset.", "default": [ "Software Engineer" ], "items": { "type": "string" } }, "searchKeywords": { "title": "Search Keywords (alias)", "type": "array", "description": "Alias for Keywords — accepts the same values. If both are provided they are merged.", "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Job location (e.g. 'Toronto, ON', 'Remote', 'United States')", "default": "Toronto, ON" }, "maxJobs": { "title": "Max Jobs to Scrape", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of job listings to scrape", "default": 50 }, "datePosted": { "title": "Date Posted Filter", "enum": [ "any", "past_24h", "past_week", "past_month" ], "type": "string", "description": "Filter jobs by when they were posted", "default": "any" }, "jobType": { "title": "Job Type", "enum": [ "any", "full_time", "part_time", "contract", "internship" ], "type": "string", "description": "Filter by job type", "default": "any" }, "experienceLevel": { "title": "Experience Level", "enum": [ "any", "internship", "entry_level", "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" }, "scrapeJobDetails": { "title": "Scrape Full Job Details", "type": "boolean", "description": "If enabled, visits each job page to get full description. Slower but more complete.", "default": true }, "proxy": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for the scraper" } } }, "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 } } } } } } } } } } ```