# Hiring Cafe Jobs Scraper (`jungle_synthesizer/hiring-cafe-scraper`) Actor Scrape global job postings from Hiring Cafe (hiring.cafe) — an AI-powered job search engine. Extract job title, company, location, salary, remote policy, seniority level, visa sponsorship, commitment type, and more from thousands of live listings. - **URL**: https://apify.com/jungle\_synthesizer/hiring-cafe-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Hiring Cafe Jobs Scraper Scrape global job postings from [Hiring Cafe](https://hiring.cafe) — an AI-powered job search engine aggregating listings from Ashby, Greenhouse, Lever, and 100+ other boards. Returns job title, company details, salary, location, remote policy, visa sponsorship, seniority level, and more for thousands of live listings worldwide. ### Hiring Cafe Scraper Features - Extracts 30+ structured fields per job posting — title, salary, location, remote policy, seniority, and more - Returns AI-normalised job titles alongside the raw listing title (useful when "Principal SWE III" means "Senior Engineer") - Collects company metadata — employee count, industries, funding stage, HQ country, and website - Captures salary data when disclosed — min/max range, currency, and original frequency (hourly, yearly) - Flags visa sponsorship availability per posting, which most job board APIs bury or omit entirely - Pure API scraping — no browser required. Fast, low-memory, no proxy needed - Handles pagination automatically across 300+ pages of results - Stops at `maxItems` so test runs don't bill you for the whole corpus ### Who Uses Hiring Cafe Data? - **Recruiters** — monitor which companies are actively hiring in target verticals before cold-outreach - **Salary researchers** — aggregate disclosed compensation ranges by role, location, and seniority for benchmarking - **Job seekers and career coaches** — track remote and visa-sponsoring positions at scale across dozens of ATS sources - **Market analysts** — identify hiring trends by company size, funding stage, or sector - **HR tech teams** — build curated job feeds from a single consolidated source rather than scraping 100+ boards separately ### How It Works 1. The scraper fetches the Hiring Cafe home page to extract the current Next.js build identifier — the key that unlocks the site's structured SSR data API. 2. It requests the data API page by page, each response containing up to 150 job objects with full AI-processed metadata. 3. Each job hit is transformed into a clean, flat record — company data merged in, salary normalised to yearly, list fields flattened to pipe-separated strings for easy CSV export. 4. Records are saved until `maxItems` is reached or the feed is exhausted. ### Hiring Cafe Scraper Input ```json { "maxItems": 100 } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | required | Maximum number of job records to return. Set to a small number for testing. | ### Hiring Cafe Scraper Output Fields ```json { "job_id": "ashby___acme-corp___a1b2c3d4-e5f6-7890-abcd-ef1234567890", "job_url": "https://jobs.ashbyhq.com/acme-corp/a1b2c3d4-e5f6-7890-abcd-ef1234567890", "job_title": "Senior Software Engineer", "job_title_raw": "Sr. Software Engineer (Backend) - Platform Team", "company_name": "Acme Corp", "company_website": "https://acmecorp.com", "company_size": 500, "company_industries": "Software | Cloud Infrastructure", "company_hq_country": "US", "company_funding_type": "Series B", "company_funding_year": 2023, "location_countries": "US | CA", "location_states": "California, US | Ontario, CA", "location_cities": "San Francisco, CA | Toronto, ON", "location_formatted": "San Francisco, CA or Toronto, ON (remote)", "remote_policy": "Hybrid", "workplace_type": "Hybrid", "seniority_level": "Senior Level", "job_category": "Engineering", "commitment": "Full Time", "role_type": "Individual Contributor", "visa_sponsorship": false, "salary_min_yearly": 160000, "salary_max_yearly": 220000, "salary_currency": "USD", "salary_listed_frequency": "Yearly", "requirements_summary": "5+ years backend engineering; Go or Rust preferred; distributed systems experience; AWS; strong systems design skills.", "technical_tools": "Go | Rust | AWS | Kubernetes | PostgreSQL", "source": "ashby", "board_token": "acme-corp", "is_expired": false, "scraped_at": "2026-06-02T10:30:00.000Z" } ``` | Field | Type | Description | |-------|------|-------------| | `job_id` | string | Unique job identifier (source + board\_token + requisition id) | | `job_url` | string | Direct application URL | | `job_title` | string | AI-normalised core job title | | `job_title_raw` | string | Original raw title from the posting | | `company_name` | string | Company name | | `company_website` | string | Company homepage URL | | `company_size` | integer | Employee headcount (approximate) | | `company_industries` | string | Industries (pipe-separated) | | `company_hq_country` | string | HQ country code (ISO) | | `company_funding_type` | string | Latest funding type (e.g. Series A, Bootstrapped) | | `company_funding_year` | integer | Year of latest funding round | | `location_countries` | string | Workplace countries (pipe-separated ISO codes) | | `location_states` | string | Workplace states/regions (pipe-separated) | | `location_cities` | string | Workplace cities (pipe-separated) | | `location_formatted` | string | Human-readable formatted workplace location | | `remote_policy` | string | Remote work policy (Remote, Hybrid, On-site) | | `workplace_type` | string | Workplace type classification | | `seniority_level` | string | Seniority (Entry Level, Mid Level, Senior Level, etc.) | | `job_category` | string | Job category/function | | `commitment` | string | Employment type (Full Time, Part Time, Contract) | | `role_type` | string | Role type (Individual Contributor, Manager) | | `visa_sponsorship` | boolean | Whether visa sponsorship is offered | | `salary_min_yearly` | number | Minimum annual salary (normalised to yearly) | | `salary_max_yearly` | number | Maximum annual salary (normalised to yearly) | | `salary_currency` | string | Salary currency code (USD, EUR, GBP, etc.) | | `salary_listed_frequency` | string | Original salary frequency (Yearly, Hourly, Monthly) | | `requirements_summary` | string | AI-generated summary of key requirements | | `technical_tools` | string | Required tools and technologies (pipe-separated) | | `source` | string | Job board source (ashby, greenhouse, lever, etc.) | | `board_token` | string | Company board token on the source platform | | `is_expired` | boolean | Whether the posting has expired | | `scraped_at` | string | ISO 8601 timestamp of when the record was scraped | ### 🔍 FAQ #### How do I scrape Hiring Cafe job postings? Hiring Cafe Jobs Scraper handles it. Set `maxItems` to however many records you need, run it, and the output lands in your dataset as structured JSON. No browser, no proxy, no configuration beyond that. #### How much does the Hiring Cafe Jobs Scraper cost to run? Billing is per record at a very low per-item rate — well under a cent per job in typical runs. The `maxItems` field caps your spend precisely if budget matters. #### What data can I get from Hiring Cafe? Hiring Cafe Jobs Scraper returns 30+ fields per posting: job title (raw and AI-normalised), company details (size, funding, industries), salary range when disclosed, location, remote policy, seniority level, visa sponsorship status, and a machine-generated requirements summary. Coverage is global — listings from every continent show up in the feed. #### Does Hiring Cafe Jobs Scraper need proxies? No. The scraper hits Hiring Cafe's SSR data API directly with a standard browser user-agent. No proxies, no CAPTCHAs, no workarounds needed. #### How many jobs are available on Hiring Cafe? The live corpus is roughly 12,000+ active listings, refreshed continuously. Set `maxItems` to a smaller number for targeted pulls, or leave it large to sweep the full feed. *** ### Need More Features? Custom filters by location, seniority, or company size? Incremental runs? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use Hiring Cafe Jobs Scraper? - **No overhead** — Pure API extraction, no browser, no proxy, no setup beyond `maxItems`. It's as simple as job scrapers get. - **Richer output than most alternatives** — AI-normalised titles, company funding data, and requirements summaries in one payload, which beats manually joining three data sources after the fact. - **Global coverage** — Listings from 100+ ATS platforms (Ashby, Greenhouse, Lever, SAP SuccessFactors, and more) in a single, consistently structured feed. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of job records to scrape. Leave empty to scrape all available jobs. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ``` # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/hiring-cafe-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/hiring-cafe-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }' | apify call jungle_synthesizer/hiring-cafe-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/hiring-cafe-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hiring Cafe Jobs Scraper", "description": "Scrape global job postings from Hiring Cafe (hiring.cafe) — an AI-powered job search engine.\nExtract job title, company, location, salary, remote policy, seniority level, visa sponsorship,\ncommitment type, and more from thousands of live listings.\n", "version": "0.1", "x-build-id": "U1QXmOE3huMmqlDLv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~hiring-cafe-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-hiring-cafe-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/jungle_synthesizer~hiring-cafe-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-hiring-cafe-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/jungle_synthesizer~hiring-cafe-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-hiring-cafe-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": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of job records to scrape. Leave empty to scrape all available jobs." } } }, "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 } } } } } } } } } } ```