# Hiring.Cafe Scraper — AI Job Aggregator (`blackfalcondata/hiringcafe-scraper`) Actor Scrape hiring.cafe — an AI-powered job aggregator with 2.8M+ listings from 46 ATS platforms worldwide. Structured salary (min/max/currency), benefits, company data, and remote filters. Incremental mode returns only new and changed jobs; compact output for AI-agent workflows. - **URL**: https://apify.com/blackfalcondata/hiringcafe-scraper.md - **Developed by:** [Black Falcon Data](https://apify.com/blackfalcondata) (community) - **Categories:** Jobs - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.80 / 1,000 results 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 ### What does Hiring.Cafe Job Scraper do? Hiring.Cafe Job Scraper extracts structured job listings data from [hiring.cafe](https://hiring.cafe). It supports keyword search, location filters, and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions) where the source provides them, incremental monitoring that only returns new or changed results on recurring runs, and a compact output mode for AI-agent and MCP workflows. ### What data can you extract from hiring.cafe? Results follow a stable schema with consistent fields across runs. In standard mode, all 85 fields are always present — unavailable data points are returned as `null`, never omitted. In compact mode, 23 agent-optimized fields are returned: job identity, location, salary with frequency, seniority, requirements summary, visa sponsorship, and source tracking. Enable detail enrichment in the input to get richer fields such as full descriptions where the source provides them. ### Input The main inputs are a search keyword, an optional location filter, and a result limit. Additional filters and options are available in the input schema. Key parameters: - **`query`** — Job search keywords. - **`country`** — Which market to search. (default: `"US"`) - **`location`** — City, state, or region. - **`maxResults`** — Maximum total results (0 = unlimited). (default: `25`) - **`includeDetails`** — Fetch full job details. (default: `true`) - **`descriptionMaxLength`** — Truncate description to N chars. 0 = no truncation. (default: `0`) - **`compact`** — Core fields only (for AI-agent/MCP workflows). (default: `false`) - **`incrementalMode`** — Compare against previous run state. (default: `false`) - **`stateKey`** — Stable identifier for tracked universe. #### Input example ```json { "query": "software engineer", "country": "US", "maxResults": 5, "includeDetails": true, "descriptionMaxLength": 0, "compact": false, "incrementalMode": false } ```` ### Output Each run produces a dataset of structured job listings records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console. #### Sample output ```json { "jobId": "ef0d6041374f0ea3b7136b71a69ad6277a13df47bb504ac39e3d819745a1ad3f", "title": "Software Developer", "company": "AM Pierce & Associates", "location": "Patuxent River, Maryland, United States", "workplaceType": "Onsite", "commitment": "Full Time", "seniorityLevel": "Senior Level", "roleType": "Individual Contributor", "roleActivities": [ "Oversee tasks", "Coordinate efforts", "Represent NAVAIR" ], "jobCategory": "Software Development", "description": "

Who We Are:<...", "salaryMin": 108000, "salaryMax": 180000, "salaryCurrency": "USD", "salaryFrequency": "Yearly", "isCompensationTransparent": true, "requirementsSummary": "Senior software developer with DoD clearance, 8+ years of experience in engineering/software, plus 3+ years in acquisition software engineering; strong Interfacing and system integration skills.", "technicalTools": [ "Software Development", "Open Architecture", "Systems Integration" ], "minYearsExperience": 8, "minManagementYears": null, "degreeRequirement": "Bachelors", "degreeFieldsOfStudy": [ "Computer Science", "Software Engineering", "Electrical Engineering" ], "licensesOrCertifications": [ "dod secret clearance" ], "languageRequirements": [ "English" ], "securityClearance": "Secret", "driverLicenseRequired": false, "retirement401kMatching": true, "retirementPlan": true, "tuitionReimbursement": false, "generousParentalLeave": false, "generousPaidTimeOff": false, "fourDayWorkWeek": false, "visaSponsorship": false, "relocationAssistance": false, "fairChance": true, "militaryVeterans": true, "physicalLaborIntensity": "Low", "physicalPosition": "Sitting", "workplaceEnvironment": "Office", "computerUsage": "High", "cognitiveDemand": "High", "oralCommunicationLevel": "Medium", "overtimeRequired": false, "onCallRequirement": null, "airTravelRequirement": null, "landTravelRequirement": "Minimal", "morningShiftWork": null, "eveningShiftWork": null, "overnightWork": null, "weekendAvailabilityRequired": false, "holidayAvailabilityRequired": false, "positionEmployerType": "External Position", "workplaceCountries": [ "US" ], "workplaceContinents": [ "North America" ], "workplaceStates": [ "Maryland, US" ], "workplaceCities": [ "Patuxent River, Maryland, US" ], "workplaceCounties": [ "Saint Marys County, Maryland, US" ], "isWorkplaceWorldwideOk": false, "latitude": 38.2709555, "longitude": -76.4369803, "companyName": "AM Pierce and Associates", "companyWebsite": "ampierce.com", "companySector": "Information Technology", "companyIndustries": [ "Aerospace and Defense", "Information Technology" ], "companyActivities": [ "Engineering", "Research" ], "companyTagline": "A woman-owned small business providing Engineering & Research, Cyber, C5ISR, Program & Acquisition Management services and solutions to government and industry.", "companyEmployeeCount": 120, "companyHqCountry": "US", "companyYearFounded": 2007, "companyOrganizationType": "Private", "companyParent": null, "companySubsidiaries": [ "Applied Technologies Group" ], "companyStockExchange": null, "companyStockSymbol": null, "companyFundingType": null, "companyFundingYear": null, "companyFundingAmount": null, "companyFundingInvestors": null, "applyUrl": "https://ampierce.applicantpro.com/jobs/4034720.html", "portalUrl": "https://hiring.cafe/viewjob/gxxqfjhrbsdfualt", "sourceAts": "applicantpro", "postedDate": "2026-03-26T00:00:00.000Z", "scrapedAt": "2026-04-05T13:50:10.906Z", "source": "hiring.cafe", "changeType": null } ``` ### How to scrape hiring.cafe 1. Go to [Hiring.Cafe Job Scraper](https://apify.com/blackfalcondata/hiringcafe-scraper) in Apify Console. 2. Enter a search keyword and optional location filter. 3. Set `maxResults` to control how many results you need. 4. Enable `includeDetails` if you need full descriptions, contact info, or company data. 5. Click **Start** and wait for the run to finish. 6. Export the dataset as JSON, CSV, or Excel. ### Use cases - Extract job listings data from hiring.cafe for market research and competitive analysis. - Monitor new and changed listings on scheduled runs without processing the full dataset every time. - Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode. - Export clean, structured data to dashboards, spreadsheets, or data warehouses. ### How much does it cost to scrape hiring.cafe? Hiring.Cafe Job Scraper uses [pay-per-event](https://docs.apify.com/platform/actors/paid-actors/pay-per-event) pricing. You pay a small fee when the run starts and then for each result that is actually produced. - **Run start:** $0.005 per run - **Per result:** $0.0018 per job listings record Example costs: - 10 results: **$0.023** - 100 results: **$0.185** - 500 results: **$0.905** Incremental runs only charge for new or changed results, making recurring monitoring cost-efficient. ### FAQ #### How many results can I get from hiring.cafe? The number of results depends on the search query and available listings on hiring.cafe. Use the `maxResults` parameter to control how many results are returned per run. #### Does Hiring.Cafe Job Scraper support recurring monitoring? Yes. Enable incremental mode to only receive new or changed listings on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset. #### Can I integrate Hiring.Cafe Job Scraper with other apps? Yes. Hiring.Cafe Job Scraper works with Apify's [integrations](https://apify.com/integrations) to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes. #### Can I use Hiring.Cafe Job Scraper with the Apify API? Yes. You can start runs, manage inputs, and retrieve results programmatically through the [Apify API](https://docs.apify.com/api/v2). Client libraries are available for JavaScript, Python, and other languages. #### Can I use Hiring.Cafe Job Scraper through an MCP Server? Yes. Apify provides an [MCP Server](https://apify.com/apify/actors-mcp-server) that lets AI assistants and agents call this actor directly. Use compact mode and `descriptionMaxLength` to keep payloads manageable for LLM context windows. #### Is it legal to scrape hiring.cafe? This actor extracts publicly available data from hiring.cafe. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant. #### Your feedback If you have questions, need a feature, or found a bug, please [open an issue](https://apify.com/blackfalcondata/hiringcafe-scraper/issues) on the actor's page in Apify Console. Your feedback helps us improve. ### You might also like - [Adzuna Job Scraper](https://apify.com/blackfalcondata/adzuna-scraper) — Scrape adzuna.com — the global job board with 20+ country markets. Structured salary. - [Arbeitsagentur Scraper](https://apify.com/blackfalcondata/arbeitsagentur-scraper) — Scrape arbeitsagentur.de — Germany's official employment portal with 1M+ listings. Contact data,. - [Bayt.com Scraper — Jobs from the Middle East](https://apify.com/blackfalcondata/bayt-scraper) — Scrape bayt.com — the leading Middle East job board. Salary data, experience requirements. - [Bumeran Scraper](https://apify.com/blackfalcondata/bumeran-scraper) — Scrape bumeran.com.ar — the largest job board across 8 LATAM countries. Work modality, contract. - [Cadremploi Job Scraper](https://apify.com/blackfalcondata/cadremploi-scraper) — Scrape cadremploi.fr — French management and executive jobs. Salary ranges, apply links. - [Dice.com Job Scraper](https://apify.com/blackfalcondata/dice-com-job-scraper) — Scrape dice.com — the leading U.S. tech job board. Structured salary (min/max/currency),. - [Drushim Scraper - Israel Job Listings from drushim.co.il](https://apify.com/blackfalcondata/drushim-scraper) — Scrape drushim.co.il — Israel's leading job board. Geo-coordinates per listing, multi-filter search. - [Duunitori Scraper](https://apify.com/blackfalcondata/duunitori-scraper) — Scrape duunitori.fi — Finland's largest job board with 22,000+ listings. Salary ranges, employment. # Actor input Schema ## `query` (type: `string`): Job search keywords. ## `country` (type: `string`): Which market to search. ## `location` (type: `string`): City, state, or region. ## `maxResults` (type: `integer`): Maximum total results (0 = unlimited). Memory scales automatically: 256 MB up to 1000, 512 MB up to 2000, 1024 MB above. ## `includeDetails` (type: `boolean`): Fetch full job details. ## `descriptionMaxLength` (type: `integer`): Truncate description to N chars. 0 = no truncation. ## `compact` (type: `boolean`): Core fields only (for AI-agent/MCP workflows). ## `incrementalMode` (type: `boolean`): Compare against previous run state. ## `stateKey` (type: `string`): Stable identifier for tracked universe. ## Actor input object example ```json { "query": "software engineer", "country": "US", "maxResults": 5, "includeDetails": true, "descriptionMaxLength": 0, "compact": false, "incrementalMode": false } ``` # 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 = { "query": "software engineer", "maxResults": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("blackfalcondata/hiringcafe-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", "maxResults": 5, } # Run the Actor and wait for it to finish run = client.actor("blackfalcondata/hiringcafe-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", "maxResults": 5 }' | apify call blackfalcondata/hiringcafe-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=blackfalcondata/hiringcafe-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hiring.Cafe Scraper — AI Job Aggregator", "description": "Scrape hiring.cafe — an AI-powered job aggregator with 2.8M+ listings from 46 ATS platforms worldwide. Structured salary (min/max/currency), benefits, company data, and remote filters. Incremental mode returns only new and changed jobs; compact output for AI-agent workflows.", "version": "0.1", "x-build-id": "4UoDfnSYMGxQwT7Ch" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/blackfalcondata~hiringcafe-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-blackfalcondata-hiringcafe-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/blackfalcondata~hiringcafe-scraper/runs": { "post": { "operationId": "runs-sync-blackfalcondata-hiringcafe-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/blackfalcondata~hiringcafe-scraper/run-sync": { "post": { "operationId": "run-sync-blackfalcondata-hiringcafe-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Search Query", "type": "string", "description": "Job search keywords." }, "country": { "title": "Country", "enum": [ "US" ], "type": "string", "description": "Which market to search.", "default": "US" }, "location": { "title": "Location", "type": "string", "description": "City, state, or region." }, "maxResults": { "title": "Max Results", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Maximum total results (0 = unlimited). Memory scales automatically: 256 MB up to 1000, 512 MB up to 2000, 1024 MB above.", "default": 25 }, "includeDetails": { "title": "Include Details", "type": "boolean", "description": "Fetch full job details.", "default": true }, "descriptionMaxLength": { "title": "Description Max Length", "minimum": 0, "type": "integer", "description": "Truncate description to N chars. 0 = no truncation.", "default": 0 }, "compact": { "title": "Compact Output", "type": "boolean", "description": "Core fields only (for AI-agent/MCP workflows).", "default": false }, "incrementalMode": { "title": "Incremental Mode", "type": "boolean", "description": "Compare against previous run state.", "default": false }, "stateKey": { "title": "State Key", "type": "string", "description": "Stable identifier for tracked universe." } } }, "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 } } } } } } } } } } ```