# Job Salary Aggregator (`intelscrape/job-salary-aggregator`) Actor Aggregate job listings with salary data from public job boards. Returns title, company, location, salary range, and apply URL. - **URL**: https://apify.com/intelscrape/job-salary-aggregator.md - **Developed by:** [IntelScrape](https://apify.com/intelscrape) (community) - **Categories:** Jobs, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Job Salary Aggregator — Only Jobs With Actual Salary Ranges Find jobs that actually show their pay. No "competitive salary" mystery ranges. Aggregates from RemoteOK, Indeed, and BuiltIn — **only returns listings with explicit salary numbers**. Calculates annual midpoint and assigns salary tier grades. ### Salary tiers - 💎 ELITE — $300K+ - 🏆 EXEC — $200K–$299K - 🥇 SENIOR — $150K–$199K - 🥈 MID — $100K–$149K - 🥉 JUNIOR — $60K–$99K ### What you get - Job title, company, location, remote status - Salary range (min/max), annual midpoint - Salary tier grade - Job description, required skills - Direct application URL - Posted date, days since posted ### Use cases - **Salary benchmarking** — know your market rate - **Compensation analysis** — HR teams building salary bands - **Recruiting tools** — find high-paying roles for candidates - **Job board aggregation** — build niche salary-focused job boards - **Career coaching** — show clients exactly what roles pay ### Sample output ```json { "title": "Senior Software Engineer", "company": "Stripe", "location": "San Francisco, CA", "remote": true, "salaryMin": 200000, "salaryMax": 280000, "salaryMidpoint": 240000, "salaryGrade": "🏆 EXEC ($200K-$299K)", "skills": ["Python", "Go", "Distributed Systems"], "url": "https://stripe.com/jobs/123" } ```` ### Keywords job salary scraper, salary aggregator, jobs with salary ranges, compensation data scraper, salary benchmark tool, Indeed salary scraper, RemoteOK scraper, remote job salaries, software engineer salary, salary transparency, job market data ### Schedule Run daily or weekly — job postings change constantly. Each run surfaces fresh listings with salary data. *** ### ?? More Actors by This Developer | Actor | What it does | |-------|-------------| | **LinkedIn Lead Scraper** | Find the candidates to hire at the salaries you're offering | | **Gov Contract Scraper** | Government contract salaries � cleared worker compensation data | | **Amazon FBA Research Tool** | E-commerce side hustle income data to complement job market research | | **Content Freshness Auditor** | Audit your careers page and job description content quality | > ?? **Power combo**: Job Salary Aggregator (know the market) ? LinkedIn Leads (find candidates) ? use together for comp benchmarking. # Actor input Schema ## `jobTitles` (type: `array`): Professional roles to look for ## `locations` (type: `array`): e.g. \["United States", "Remote", "New York, NY"] ## `minSalary` (type: `integer`): Minimum annual salary ($) ## `remoteOnly` (type: `boolean`): Remote jobs only ## `maxResults` (type: `integer`): Max results ## Actor input object example ```json { "jobTitles": [ "software engineer", "data scientist", "product manager" ], "locations": [ "United States", "Remote" ], "minSalary": 80000, "remoteOnly": false, "maxResults": 200 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("intelscrape/job-salary-aggregator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("intelscrape/job-salary-aggregator").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 '{}' | apify call intelscrape/job-salary-aggregator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=intelscrape/job-salary-aggregator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Job Salary Aggregator", "description": "Aggregate job listings with salary data from public job boards. Returns title, company, location, salary range, and apply URL.", "version": "1.0", "x-build-id": "OFsJLp1p8l02PsBC1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/intelscrape~job-salary-aggregator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-intelscrape-job-salary-aggregator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/intelscrape~job-salary-aggregator/runs": { "post": { "operationId": "runs-sync-intelscrape-job-salary-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/intelscrape~job-salary-aggregator/run-sync": { "post": { "operationId": "run-sync-intelscrape-job-salary-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "jobTitles": { "title": "Job titles to search", "type": "array", "description": "Professional roles to look for", "items": { "type": "string" }, "default": [ "software engineer", "data scientist", "product manager" ] }, "locations": { "title": "Locations", "type": "array", "description": "e.g. [\"United States\", \"Remote\", \"New York, NY\"]", "items": { "type": "string" }, "default": [ "United States", "Remote" ] }, "minSalary": { "title": "Minimum annual salary ($)", "minimum": 0, "type": "integer", "description": "Minimum annual salary ($)", "default": 80000 }, "remoteOnly": { "title": "Remote jobs only", "type": "boolean", "description": "Remote jobs only", "default": false }, "maxResults": { "title": "Max results", "minimum": 10, "maximum": 1000, "type": "integer", "description": "Max results", "default": 200 } } }, "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 } } } } } } } } } } ```