# Google Jobs Scraper (`codingfrontend/google-jobs-scraper`) Actor Scrape job listings from Google Jobs search including title, company, location, salary, and description - **URL**: https://apify.com/codingfrontend/google-jobs-scraper.md - **Developed by:** [codingfrontend](https://apify.com/codingfrontend) (community) - **Categories:** Jobs, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Google Jobs Scraper ### Features - **Job Listings**: Extracts comprehensive job listing data from Google Jobs search results including title, company, and location - **Salary Information**: Captures salary ranges and compensation details when available - **Job Type & Date**: Extracts employment type (Full-time, Part-time, Contract) and posting date - **Full Job Descriptions**: Retrieves the complete job description text from each listing - **Apply Links**: Collects the primary and all available application URLs per job - **Source Tracking**: Records which job board or employer page each listing originates from - **Localization Support**: Supports country and language codes for localized Google Jobs results - **Proxy Support**: Built-in Apify Proxy support with residential proxies to avoid blocks ### Input Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `query` | String | Yes | `"software engineer"` | Job title or keywords to search for | | `location` | String | No | `"San Francisco"` | Job location (city, state, country) | | `maxItems` | Integer | No | `50` | Maximum number of job listings to scrape (1–500) | | `gl` | String | No | `"us"` | Country code for localized results (e.g., us, in, gb) | | `hl` | String | No | `"en"` | Language code (e.g., en, es, fr) | | `proxyConfiguration` | Object | No | Apify Residential | Proxy settings for the scraper | #### Input Schema Example ```json { "query": "data scientist", "location": "New York", "maxItems": 20, "gl": "us", "hl": "en", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ```` ### Output Schema The scraper outputs structured JSON data for each job listing found on Google Jobs. #### Main Fields | Field | Type | Description | |-------|------|-------------| | `position` | Integer | Result position in the search | | `jobTitle` | String | Title of the job listing | | `company` | String | Company offering the job | | `location` | String | Job location | | `salary` | String | Salary information (if available) | | `datePosted` | String | When the job was posted | | `jobType` | String | Employment type (Full-time, Part-time, Contract, etc.) | | `description` | String | Full job description text | | `applyLink` | String | Primary URL to apply for the job | | `applyLinks` | Array | All available apply links with text and URL | | `source` | String | Job listing source (e.g., LinkedIn, Indeed, company site) | | `searchQuery` | String | The search query used | | `searchLocation` | String | Location searched | | `scrapedAt` | String | ISO timestamp of when the data was scraped | #### Job Listing Example ```json { "position": 1, "jobTitle": "Senior Data Scientist", "company": "Google", "location": "New York, NY", "salary": "$150,000 - $200,000 a year", "datePosted": "3 days ago", "jobType": "Full-time", "description": "We are looking for a Senior Data Scientist to join our team...", "applyLink": "https://careers.google.com/jobs/results/12345", "applyLinks": [ { "text": "Apply on Google Careers", "url": "https://careers.google.com/jobs/results/12345" }, { "text": "Apply on LinkedIn", "url": "https://www.linkedin.com/jobs/view/12345" } ], "source": "Google Careers", "searchQuery": "data scientist", "searchLocation": "New York", "scrapedAt": "2025-01-15T10:30:00.000Z" } ``` # Actor input Schema ## `query` (type: `string`): Job title or keywords to search for ## `location` (type: `string`): Job location (city, state, country) ## `maxItems` (type: `integer`): Maximum number of job listings to scrape ## `gl` (type: `string`): Country code for localized results ## `hl` (type: `string`): Language code ## `proxyConfiguration` (type: `object`): Use Apify Proxy or custom proxy ## Actor input object example ```json { "query": "software engineer", "location": "San Francisco", "maxItems": 50, "gl": "us", "hl": "en", "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 = { "query": "software engineer", "location": "San Francisco", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("codingfrontend/google-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 = { "query": "software engineer", "location": "San Francisco", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("codingfrontend/google-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 '{ "query": "software engineer", "location": "San Francisco", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call codingfrontend/google-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=codingfrontend/google-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Jobs Scraper", "description": "Scrape job listings from Google Jobs search including title, company, location, salary, and description", "version": "0.0", "x-build-id": "MbYiLZes26PGsJC0a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/codingfrontend~google-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-codingfrontend-google-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/codingfrontend~google-jobs-scraper/runs": { "post": { "operationId": "runs-sync-codingfrontend-google-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/codingfrontend~google-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-codingfrontend-google-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Job Search Query", "type": "string", "description": "Job title or keywords to search for", "default": "software engineer" }, "location": { "title": "Location", "type": "string", "description": "Job location (city, state, country)", "default": "San Francisco" }, "maxItems": { "title": "Maximum Items", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of job listings to scrape", "default": 50 }, "gl": { "title": "Country", "type": "string", "description": "Country code for localized results", "default": "us" }, "hl": { "title": "Language", "type": "string", "description": "Language code", "default": "en" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Use Apify Proxy or custom proxy", "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 } } } } } } } } } } ```