# Hiring Cafe Jobs Scraper (`crawlerbros/hiring-cafe-scraper`) Actor Extract global job postings from hiring.cafe including title, company, salary, location, remote status, seniority, visa sponsorship, and more. - **URL**: https://apify.com/crawlerbros/hiring-cafe-scraper.md - **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community) - **Categories:** Jobs, Automation, Developer tools - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 12 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $3.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Extract global job postings from **hiring.cafe** — an AI-powered job aggregator that indexes 2.9+ million listings from Greenhouse, Lever, Workable, Workday, SuccessFactors, Hirebridge, BambooHR, and 14,000+ direct company career pages. Returns **32 structured fields** per job including title, company, workplace type, seniority, salary (when disclosed), geolocation, required technical tools, company industries, and a direct apply URL. ### Features - **32 output fields** per job — complete flat schema with typed defaults (zero nulls) - **Direct apply URLs** — every job links back to the original ATS posting - **Rich metadata** — workplace type (Remote/Hybrid/Onsite), seniority, commitment, category, required technical tools, min years experience, salary range when disclosed - **Geolocation** — latitude/longitude per listing - **Company enrichment** — hiring company name, website, industries, HQ country, employee count bucket - **Filter support** — keyword search, workplace type, seniority level, commitment type, date range - **Hardcoded RESIDENTIAL US proxy** — required to bypass Cloudflare Managed Challenge - **Automatic Cloudflare bypass** — Patchright Chromium session with session rotation (typically solves in 8–20 seconds) ### Input | Field | Type | Description | |---|---|---| | `searchQueries` | Array of strings | Keywords to search on hiring.cafe (e.g., `"software engineer"`, `"data scientist"`). Empty array returns unfiltered results. | | `workplaceTypes` | Array | Filter by workplace type: `Remote`, `Hybrid`, `Onsite` (default: all three) | | `seniorityLevels` | Array | `No Prior Experience Required`, `Entry Level`, `Mid Level`, `Senior Level`, `Director`, `Executive` (default: all six) | | `commitmentTypes` | Array | `Full Time`, `Part Time`, `Contract`, `Internship`, `Temporary`, `Seasonal`, `Volunteer` (default: all seven) | | `dateFetchedPastNDays` | Integer | Only include jobs fetched within the last N days (default 30, max 365) | | `maxItems` | Integer | Maximum jobs to return across all queries (default 50, max 1000) | #### Example Input ```json { "searchQueries": ["software engineer", "data scientist"], "workplaceTypes": ["Remote", "Hybrid"], "seniorityLevels": ["Mid Level", "Senior Level"], "commitmentTypes": ["Full Time"], "dateFetchedPastNDays": 7, "maxItems": 100 } ```` Minimal input (just a keyword): ```json { "searchQueries": ["nurse"], "maxItems": 20 } ``` ### Output Each job has **32 fields**. Every field is always present with a typed default (empty string, zero, empty list, or `false`) — **never `null`**. #### Identity | Field | Type | Description | |---|---|---| | `id` | String | Unique job ID | | `objectID` | String | Search index object ID | | `source` | String | Source ATS (`greenhouse`, `lever`, `workable`, `successfactors`, `hirebridge`, ...) | | `boardToken` | String | Job board token on that ATS | | `applyUrl` | String | Direct apply URL (redirects to original source) | | `title` | String | Job title | | `description` | String | Job description (HTML stripped, truncated to 2,000 chars) | | `isExpired` | Boolean | Whether the listing is expired | #### Classification | Field | Type | Description | |---|---|---| | `coreJobTitle` | String | Canonicalized core job title | | `category` | String | Job category (e.g., `Engineering`, `Data and Analytics`, `Marketing`) | | `seniorityLevel` | String | Comma-joined seniority tags (e.g., `"Mid Level"`, `"Senior Level"`) | | `roleType` | String | `Individual Contributor` or `People Manager` | | `commitment` | String | Commitment type (e.g., `"Full Time"`) | | `workplaceType` | String | `Remote`, `Hybrid`, or `Onsite` | #### Location | Field | Type | Description | |---|---|---| | `workplaceCountries` | Array | Country codes (e.g., `["US", "GB"]`) | | `workplaceStates` | Array | State / region names | | `workplaceCities` | Array | City names | | `latitude` | Number | Primary location latitude | | `longitude` | Number | Primary location longitude | #### Compensation & Requirements | Field | Type | Description | |---|---|---| | `salaryMin` | Number | Minimum salary (yearly, if disclosed; `0` otherwise) | | `salaryMax` | Number | Maximum salary (yearly, if disclosed) | | `salaryCurrency` | String | Salary currency code | | `salaryFrequency` | String | `Yearly`, `Hourly`, `Monthly`, etc. | | `technicalTools` | Array | Required technologies / tools | | `minYearsExperience` | Integer | Minimum YoE required (`0` if not specified) | | `bachelorsDegreeRequirement` | String | Bachelor's degree requirement level | #### Company | Field | Type | Description | |---|---|---| | `companyName` | String | Hiring company name | | `companyWebsite` | String | Company homepage URL | | `companyIndustries` | Array | Industry tags | | `companyEmployees` | String | Employee count bucket (e.g., `"1001-5000"`) | | `companyHqCountry` | String | Company HQ country code | #### Metadata | Field | Type | Description | |---|---|---| | `scrapedAt` | String | ISO 8601 scrape timestamp | #### Example Output ```json { "id": "greenhouse___acme___4567890", "objectID": "greenhouse_acme_4567890", "source": "greenhouse", "boardToken": "acme", "applyUrl": "https://boards.greenhouse.io/acme/jobs/4567890", "title": "Senior Software Engineer, Backend", "description": "We are looking for a senior backend engineer to join our Platform team...", "coreJobTitle": "Software Engineer", "category": "Engineering", "seniorityLevel": "Senior Level", "roleType": "Individual Contributor", "commitment": "Full Time", "workplaceType": "Remote", "workplaceCountries": ["US"], "workplaceStates": ["California", "New York"], "workplaceCities": ["San Francisco", "New York"], "latitude": 37.7749, "longitude": -122.4194, "salaryMin": 160000.0, "salaryMax": 220000.0, "salaryCurrency": "USD", "salaryFrequency": "Yearly", "technicalTools": ["Python", "PostgreSQL", "Kubernetes", "Django"], "minYearsExperience": 5, "bachelorsDegreeRequirement": "Required", "companyName": "Acme Corp", "companyWebsite": "https://acme.example.com", "companyIndustries": ["Software", "SaaS"], "companyEmployees": "1001-5000", "companyHqCountry": "US", "isExpired": false, "scrapedAt": "2026-04-11T11:05:00+00:00" } ``` ### FAQ **Q: Why is a RESIDENTIAL proxy required?** Hiring.cafe uses Cloudflare Managed Challenge (Turnstile) which blocks all Apify datacenter IPs with `403 Just a moment...`. A real residential IP + a Chrome browser session with a ~10–20 second challenge-solve wait is needed. The proxy is hardcoded and applied automatically — no configuration needed from your side. **Q: How does the Cloudflare bypass work?** The scraper launches a Patchright Chromium browser on a RESIDENTIAL US proxy session, navigates to hiring.cafe, and waits for the Cloudflare managed challenge to self-solve (typically 8–20 seconds). Once `document.title` changes from `"Just a moment..."` to `"HiringCafe - AI Job Search"`, the browser has a valid `cf_clearance` cookie. All API calls are then made via in-browser `fetch()` so the Cloudflare session cookies are reused. If the first attempt doesn't solve within 60 seconds, the scraper rotates the proxy session and tries again (up to 6 attempts). **Q: How does pagination work?** The API returns ~120–160 jobs per page. The scraper walks pages via `&page=N` until `maxItems` is reached or results run out. **Q: Why are some `salaryMin` / `salaryMax` values zero?** Fewer than half of listings disclose salary on hiring.cafe. When not disclosed, both fields are `0.0` (typed default, not `null`). **Q: Can I search by company?** Not yet in this scraper's input schema — you can achieve it by putting the company name in `searchQueries`, which matches against job title, description, AND company name. **Q: What's the difference between `seniorityLevel` and `minYearsExperience`?** `seniorityLevel` is the categorical tag (`Mid Level`, `Senior Level`, etc.) assigned by hiring.cafe's classifier. `minYearsExperience` is the numeric minimum YoE extracted from the job description (may be `0` if not specified). **Q: Are expired jobs included?** No — by default hiring.cafe only returns active listings (`isExpired: false`). The field is preserved in the output for consistency. **Q: How fresh is the data?** `dateFetchedPastNDays` controls freshness. Default is 30 days. Set to `7` for last-week postings, or `1` for last-24-hours monitoring. ### Use Cases - **Talent intelligence** — monitor hiring velocity for specific roles, companies, or regions - **Compensation research** — aggregate salary ranges by role, seniority, and location (where disclosed) - **Remote-work trends** — filter by `workplaceTypes: ["Remote"]` to track the remote-first market - **ATS market share analysis** — group by `source` to see which ATS platforms are most used - **Skills demand tracking** — aggregate `technicalTools` frequencies to spot rising technologies - **Job alerts** — daily runs with narrow filters (e.g., `["python developer", "Remote"]`) to monitor new postings - **Recruitment pipelines** — bulk-import matching listings into CRMs with all 32 fields ready to use # Actor input Schema ## `searchQueries` (type: `array`): Keywords to search on hiring.cafe (e.g., 'software engineer', 'data scientist'). Empty array or empty string returns unfiltered results. ## `workplaceTypes` (type: `array`): Filter by workplace type. Defaults to all three. ## `seniorityLevels` (type: `array`): Filter by seniority level. ## `commitmentTypes` (type: `array`): Filter by employment commitment. ## `dateFetchedPastNDays` (type: `integer`): Only include jobs fetched within the last N days. ## `maxItems` (type: `integer`): Maximum number of jobs to return across all queries. ## Actor input object example ```json { "searchQueries": [ "software engineer" ], "workplaceTypes": [ "Remote", "Hybrid", "Onsite" ], "seniorityLevels": [ "No Prior Experience Required", "Entry Level", "Mid Level", "Senior Level", "Director", "Executive" ], "commitmentTypes": [ "Full Time", "Part Time", "Contract", "Internship", "Temporary", "Seasonal", "Volunteer" ], "dateFetchedPastNDays": 30, "maxItems": 3 } ``` # Actor output Schema ## `jobs` (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 = { "searchQueries": [ "software engineer" ], "maxItems": 3 }; // Run the Actor and wait for it to finish const run = await client.actor("crawlerbros/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 = { "searchQueries": ["software engineer"], "maxItems": 3, } # Run the Actor and wait for it to finish run = client.actor("crawlerbros/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 '{ "searchQueries": [ "software engineer" ], "maxItems": 3 }' | apify call crawlerbros/hiring-cafe-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerbros/hiring-cafe-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hiring Cafe Jobs Scraper", "description": "Extract global job postings from hiring.cafe including title, company, salary, location, remote status, seniority, visa sponsorship, and more.", "version": "1.0", "x-build-id": "NsVBongyCuKOIE0QZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerbros~hiring-cafe-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerbros-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawlerbros~hiring-cafe-scraper/runs": { "post": { "operationId": "runs-sync-crawlerbros-hiring-cafe-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawlerbros~hiring-cafe-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerbros-hiring-cafe-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "Keywords to search on hiring.cafe (e.g., 'software engineer', 'data scientist'). Empty array or empty string returns unfiltered results.", "items": { "type": "string" } }, "workplaceTypes": { "title": "Workplace Types", "type": "array", "description": "Filter by workplace type. Defaults to all three.", "items": { "type": "string", "enum": [ "Remote", "Hybrid", "Onsite" ] }, "default": [ "Remote", "Hybrid", "Onsite" ] }, "seniorityLevels": { "title": "Seniority Levels", "type": "array", "description": "Filter by seniority level.", "items": { "type": "string", "enum": [ "No Prior Experience Required", "Entry Level", "Mid Level", "Senior Level", "Director", "Executive" ] }, "default": [ "No Prior Experience Required", "Entry Level", "Mid Level", "Senior Level", "Director", "Executive" ] }, "commitmentTypes": { "title": "Commitment Types", "type": "array", "description": "Filter by employment commitment.", "items": { "type": "string", "enum": [ "Full Time", "Part Time", "Contract", "Internship", "Temporary", "Seasonal", "Volunteer" ] }, "default": [ "Full Time", "Part Time", "Contract", "Internship", "Temporary", "Seasonal", "Volunteer" ] }, "dateFetchedPastNDays": { "title": "Posted Within (days)", "minimum": 1, "maximum": 365, "type": "integer", "description": "Only include jobs fetched within the last N days.", "default": 30 }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of jobs to return across all queries.", "default": 50 } } }, "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 } } } } } } } } } } ```