# Google Careers Scraper (`scrapeai/google-careers-scraper`) Actor Google Careers Scraper extracts job listings from the Google Careers website. It collects details such as job title, location, department, job ID, description, and application links. Ideal for job market analysis, recruitment research, and building job databases from Google Careers pages. - **URL**: https://apify.com/scrapeai/google-careers-scraper.md - **Developed by:** [ScrapeAI](https://apify.com/scrapeai) (community) - **Categories:** Jobs, Developer tools - **Stats:** 14 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing $5.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 Careers Jobs Scraper Actor for scraping job listings from [Google Careers]. It searches for jobs by query and location, extracting title, company, salary, requirements, application link, and more. ### What It Does This scraper searches Google Careers for your query and location and extracts detailed information about each job listing. Perfect for job market research, recruiting analysis, and career exploration. ### Input | Field | Type | Description | Default | |---|---|---|---| | query | String | Job search query (e.g. "Software Engineer") | "Software Engineer" | | location | String | Location filter (e.g. "San Francisco, CA, USA") | "San Francisco, CA, USA" | | maxResults | Number | Maximum number of jobs to scrape | 20 | | proxyConfiguration | Object | Proxy settings | Apify Residential | ### Example Input ```json { "query": "Software Engineer", "location": "San Francisco, CA, USA", "maxResults": 20 } ```` ### Output Each job result includes: | Field | Type | Description | |---|---|---| | position | Number | Position in the results | | title | String | Job title | | company\_name | String | Name of the hiring company | | location | String | Job location | | description | String | Job description snippet | | salary | String | Salary or compensation range | | job\_type | String | Employment type (Full-time, Part-time, Contract, etc.) | | posted\_date | String | When the job was posted (e.g. "2 days ago") | | application\_link | String | Direct URL to apply for the job | | company\_logo | String | URL of the company logo image | | requirements | Array | List of job requirements or qualifications | | query | String | The search query used | | search\_location | String | The location filter used | | scrapedAt | String | ISO timestamp of when the data was scraped | ### Example Output ```json { "position": 1, "title": "Senior Software Engineer", "company_name": "Google", "location": "San Francisco, CA", "description": "We are looking for a talented engineer to join our team and work on large-scale distributed systems.", "salary": "$150,000 - $200,000", "job_type": "Full-time", "posted_date": "1 day ago", "application_link": "https://www.google.com/about/careers/applications/jobs/results/12345678901234567", "company_logo": "https://logo.url", "requirements": [ "5+ years of software engineering experience", "Proficiency in Python, Java, or Go", "Experience with distributed systems", "Strong problem-solving skills" ], "query": "Software Engineer", "search_location": "San Francisco, CA, USA", "scrapedAt": "2026-03-10T10:00:00.000Z" } ``` ### Supported Features - Search for jobs by keyword query (e.g. "Software Engineer", "Product Manager", "Data Scientist") - Filter by location (city, state, country) - Extracts salary, job type, requirements, and application links - Automatic scroll to load more job results - Handles Google consent dialogs automatically - Residential proxy support for reliable scraping ### Tips - Use specific titles like "Senior Software Engineer" or "ML Engineer" for targeted results - Set `location` to a city or region to filter by geography (e.g. "New York, NY, USA", "London, UK") - Leave `location` empty to search globally - Start with a small `maxResults` value to test before running larger scrapes - Use Apify Proxy with the RESIDENTIAL group for best results and to avoid blocks # Actor input Schema ## `query` (type: `string`): The job search query (e.g. 'Software Engineer', 'Product Manager', 'Data Scientist') ## `location` (type: `string`): Job location to filter results (e.g. 'San Francisco, CA, USA', 'New York, NY, USA', 'London, UK') ## `maxResults` (type: `integer`): Maximum number of job results to retrieve ## `proxyConfiguration` (type: `object`): Proxy configuration for the scraper. Use Apify Proxy with RESIDENTIAL group for best results. ## Actor input object example ```json { "query": "Software Engineer", "location": "San Francisco, CA, USA", "maxResults": 10, "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, CA, USA", "maxResults": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeai/google-careers-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, CA, USA", "maxResults": 10, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("scrapeai/google-careers-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, CA, USA", "maxResults": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call scrapeai/google-careers-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeai/google-careers-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Careers Scraper", "description": "Google Careers Scraper extracts job listings from the Google Careers website. It collects details such as job title, location, department, job ID, description, and application links. Ideal for job market analysis, recruitment research, and building job databases from Google Careers pages.", "version": "0.0", "x-build-id": "XBhf7KsQgirWbR8R0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeai~google-careers-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeai-google-careers-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeai~google-careers-scraper/runs": { "post": { "operationId": "runs-sync-scrapeai-google-careers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeai~google-careers-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeai-google-careers-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "The job search query (e.g. 'Software Engineer', 'Product Manager', 'Data Scientist')", "default": "Software Engineer" }, "location": { "title": "Location", "type": "string", "description": "Job location to filter results (e.g. 'San Francisco, CA, USA', 'New York, NY, USA', 'London, UK')", "default": "San Francisco, CA, USA" }, "maxResults": { "title": "Maximum Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of job results to retrieve", "default": 10 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy configuration for the scraper. Use Apify Proxy with RESIDENTIAL group for best results.", "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 } } } } } } } } } } ```