# LinkedIn Jobs Scraper — No Login, No Cookies (`nomad-jobs/linkedin-scraper`) Actor Scrape LinkedIn job listings without login or cookies. Filter by keyword, location, remote and posting age, and get clean JSON with title, company, location, salary and apply URL. Ideal for job search automation, market research and recruiting pipelines. - **URL**: https://apify.com/nomad-jobs/linkedin-scraper.md - **Developed by:** [Nomad.Dev](https://apify.com/nomad-jobs) (community) - **Categories:** Jobs - **Stats:** 3 total users, 1 monthly users, 100.0% 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.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 — No Login, No Cookies Extract live job postings from LinkedIn's public job search — no account, no cookies, no browser. Filter by keyword, location, remote-only and posting age, and receive one clean JSON record per job. ### What LinkedIn jobs data does this scraper extract? Each result is one flat JSON record per job posting: | Field | Meaning | |---|---| | `title` | Job title as posted | | `company` | Hiring company / organisation | | `location` | Location / duty station (may include remote hints) | | `url` | Direct link to the posting | | `postedAt` | Posting date where the source provides it | | `salary` | Salary text where the source provides it | | `snippet` | Short description excerpt | | `id` | Stable source-side identifier | ### How to scrape LinkedIn jobs with this Actor 1. Click **Try for free** / **Run** — no login to the target site, no cookies, no proxies to configure. 2. Adjust the input (keyword, filters, `maxItems`) or keep the defaults. 3. Run it and export the dataset as JSON, CSV or Excel, or read it over the [API](https://docs.apify.com/api/v2). Run it from your own code: ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("nomad-jobs/linkedin-scraper").call(run_input={"maxItems": 50}) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["title"], "—", item["company"], item["url"]) ```` Or a single HTTP call that runs the Actor and returns items in one response: ```bash curl -X POST \ "https://api.apify.com/v2/acts/nomad-jobs~linkedin-scraper/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"maxItems": 50}' ``` ### Input | Field | Type | Default | Notes | |---|---|---|---| | `keyword` | string | `""` | Job title, skill or role to search for (e.g. "software engineer", "product manager react"). Leave empty to… | | `location` | string | `""` | City, region or country to filter by (e.g. "Spain", "London", "European Union"). Leave empty for worldwide… | | `remote` | boolean | `false` | When enabled, restricts results to remote-eligible postings. | | `timeFilter` | string | `"r86400"` | Restrict to postings published within the chosen window. | | `maxItems` | integer | `100` | Maximum number of job postings to return. Set 0 for no limit (up to ~200 per run). | | `includeDescription` | boolean | `true` | Fetch and include the full plain-text job description for each posting. Disabling this makes runs faster… | | `cacheTtlSeconds` | integer | `1800` | Cache the upstream fetch in the key-value store for this many seconds; re-runs within the window skip the… | ### Output example ```json { "id": "4056731188", "title": "Senior Frontend Engineer (React)", "company": "Acme Software", "location": "Berlin, Germany (Remote)", "url": "https://www.linkedin.com/jobs/view/4056731188", "postedAt": "2026-06-30", "salary": "", "snippet": "We are hiring a Senior Frontend Engineer..." } ``` ### Pricing Pay per event: **$0.05 per Actor start** and **$0.004 per job returned**. 100 jobs ≈ $0.45. No subscription, no rental — you pay only for what you fetch. ### Use cases - Job-alert bots and job boards that need fresh LinkedIn postings - Recruiting and sourcing pipelines tracking who is hiring - Salary and hiring-market research by role or region - AI agents that match candidates to live openings ### FAQ **Is it legal to scrape LinkedIn jobs?** This Actor reads only publicly available job postings — data any visitor can see without logging in. No personal data behind authentication is touched. Review the target site's terms and your local regulations for your specific use case. **Do I need an account on the target site?** No. Postings are fetched from public pages/APIs — no login, cookies or session tokens. **How fresh is the data?** Every run fetches live listings. Results are cached for `cacheTtlSeconds` (default 30 min, set 0 to always hit the source live). **How many jobs can I get?** `maxItems` caps the run (set 0 where supported for no cap). Most sources paginate from newest to oldest. **Does it work without a LinkedIn account?** Yes. The scraper reads LinkedIn's public guest job-search endpoint, so no login, cookies or session tokens are needed. **Something broken or missing?** Open an issue on the Actor's **Issues** tab — it is monitored and reliability fixes ship fast. ### Related Actors - [Web Developer Jobs Scraper — 10 Boards in One](https://apify.com/nomad-jobs/web-dev-bundle) - [AI & ML Engineer Jobs Scraper — 8 Boards in One](https://apify.com/nomad-jobs/ml-ai-dev-bundle) - [Research & Academic Jobs Scraper — 10 Sources](https://apify.com/nomad-jobs/researcher-bundle) - [Remote Jobs Scraper — RemoteOK Remotive WWR](https://apify.com/nomad-jobs/remote-boards-scraper) # Actor input Schema ## `keyword` (type: `string`): Job title, skill or role to search for (e.g. "software engineer", "product manager react"). Leave empty to search all recent postings for the given location. ## `location` (type: `string`): City, region or country to filter by (e.g. "Spain", "London", "European Union"). Leave empty for worldwide results. ## `remote` (type: `boolean`): When enabled, restricts results to remote-eligible postings. ## `timeFilter` (type: `string`): Restrict to postings published within the chosen window. ## `maxItems` (type: `integer`): Maximum number of job postings to return. Set 0 for no limit (up to ~200 per run). ## `includeDescription` (type: `boolean`): Fetch and include the full plain-text job description for each posting. Disabling this makes runs faster and uses fewer requests. ## `cacheTtlSeconds` (type: `integer`): Cache the upstream fetch in the key-value store for this many seconds; re-runs within the window skip the network call. Set 0 to disable. ## Actor input object example ```json { "keyword": "frontend developer typescript", "location": "Germany", "remote": false, "timeFilter": "r86400", "maxItems": 100, "includeDescription": true, "cacheTtlSeconds": 1800 } ``` # 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("nomad-jobs/linkedin-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("nomad-jobs/linkedin-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 nomad-jobs/linkedin-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nomad-jobs/linkedin-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper — No Login, No Cookies", "description": "Scrape LinkedIn job listings without login or cookies. Filter by keyword, location, remote and posting age, and get clean JSON with title, company, location, salary and apply URL. Ideal for job search automation, market research and recruiting pipelines.", "version": "0.1", "x-build-id": "Qe7IwZQyndU3J22dk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nomad-jobs~linkedin-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nomad-jobs-linkedin-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nomad-jobs~linkedin-scraper/runs": { "post": { "operationId": "runs-sync-nomad-jobs-linkedin-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nomad-jobs~linkedin-scraper/run-sync": { "post": { "operationId": "run-sync-nomad-jobs-linkedin-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keyword": { "title": "Keyword", "type": "string", "description": "Job title, skill or role to search for (e.g. \"software engineer\", \"product manager react\"). Leave empty to search all recent postings for the given location." }, "location": { "title": "Location", "type": "string", "description": "City, region or country to filter by (e.g. \"Spain\", \"London\", \"European Union\"). Leave empty for worldwide results." }, "remote": { "title": "Remote only", "type": "boolean", "description": "When enabled, restricts results to remote-eligible postings.", "default": false }, "timeFilter": { "title": "Posted within", "enum": [ "r3600", "r86400", "r604800", "r2592000" ], "type": "string", "description": "Restrict to postings published within the chosen window.", "default": "r86400" }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Maximum number of job postings to return. Set 0 for no limit (up to ~200 per run).", "default": 100 }, "includeDescription": { "title": "Include full description", "type": "boolean", "description": "Fetch and include the full plain-text job description for each posting. Disabling this makes runs faster and uses fewer requests.", "default": true }, "cacheTtlSeconds": { "title": "Cache TTL (seconds)", "minimum": 0, "type": "integer", "description": "Cache the upstream fetch in the key-value store for this many seconds; re-runs within the window skip the network call. Set 0 to disable.", "default": 1800 } } }, "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 } } } } } } } } } } ```