# Tech Salary Intelligence — Levels.fyi + H1B (`dp862/tech-salary-intelligence`) Actor - **URL**: https://apify.com/dp862/tech-salary-intelligence.md - **Developed by:** [Deepthi](https://apify.com/dp862) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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 ## Tech Salary Intelligence — Levels.fyi + H1B Compensation Scraper Scrape real tech **compensation data** from **Levels.fyi** and **US H1B wage disclosures** in a single run, merged into one clean schema. Base salary, stock, bonus, total comp, level, location and experience — the unified actor for tech pay benchmarking. ### Why this actor Comp data is scattered: Levels.fyi has self-reported tech packages (base/stock/bonus by level), H1B filings are official government wage disclosures. No single tool combines them. This does: - ✅ **2 sources, 1 schema** — `company`, `title`, `level`, `location`, `baseSalary`, `stock`, `bonus`, `totalComp`, `yearsOfExperience`, `submittedAt`. - ✅ **Self-reported + official** — Levels.fyi market data plus verifiable H1B government filings. - ✅ **Dedupe** across sources. - ✅ **Pay-per-event** — pay only for records you get. ### Input | Field | Type | Description | |---|---|---| | `company` | string | e.g. `google`, `amazon` (required for Levels.fyi) | | `role` | string | e.g. `software engineer`, `product manager` | | `location` | string | City filter for H1B (optional) | | `year` | integer | H1B filing year (optional) | | `sources` | array | `levels.fyi`, `h1b` | | `maxItemsPerSource` | integer | Cap per source (default 200) | | `dedupe` | boolean | Remove duplicates (default true) | #### Example ```json { "company": "google", "role": "software engineer", "sources": ["levels.fyi", "h1b"], "maxItemsPerSource": 200 } ```` ### Output ```json { "source": "levels.fyi", "company": "Google", "title": "Software Engineer", "level": "L3", "location": "New York, NY", "baseSalary": 150000, "stock": 25000, "bonus": 0, "totalComp": 175000, "currency": "USD", "yearsOfExperience": 0, "submittedAt": "2026-06-06", "scrapedAt": "2026-06-10T00:00:00.000Z" } ``` ### Use cases - **Recruiters & comp teams** benchmarking offers and pay bands. - **Job seekers** researching real compensation before negotiating. - **HR-tech & comp-benchmarking products** sourcing market data. - **Researchers & journalists** analysing tech pay and H1B wages. - **AI agents** — callable as an MCP tool for live comp lookups. ### Notes Scrapes publicly available compensation data only (Levels.fyi public pages; H1B is public US government disclosure data). Pure HTTP, fast, low cost. ### Pricing Pay-per-event: small flat fee per run + per-record fee. See the **Pricing** tab. # Actor input Schema ## `company` (type: `string`): Company to look up, e.g. "google", "amazon", "microsoft". Required for Levels.fyi. ## `role` (type: `string`): Job title, e.g. "software engineer", "product manager", "data scientist". ## `location` (type: `string`): City filter for H1B data, e.g. "new york". Leave blank for all. ## `year` (type: `integer`): Year of H1B wage filings to fetch. Defaults to last year. ## `sources` (type: `array`): Which compensation sources to scrape and merge. ## `maxItemsPerSource` (type: `integer`): Maximum salary records to collect from each source before merging and dedupe. ## `dedupe` (type: `boolean`): Drop duplicate salary records across sources. ## Actor input object example ```json { "company": "google", "role": "software engineer", "location": "", "sources": [ "levels.fyi" ], "maxItemsPerSource": 200, "dedupe": true } ``` # 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("dp862/tech-salary-intelligence").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("dp862/tech-salary-intelligence").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 dp862/tech-salary-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dp862/tech-salary-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tech Salary Intelligence — Levels.fyi + H1B", "version": "0.1", "x-build-id": "N5ZXOgXK3b4gByLUB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dp862~tech-salary-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dp862-tech-salary-intelligence", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dp862~tech-salary-intelligence/runs": { "post": { "operationId": "runs-sync-dp862-tech-salary-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dp862~tech-salary-intelligence/run-sync": { "post": { "operationId": "run-sync-dp862-tech-salary-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "company": { "title": "Company", "type": "string", "description": "Company to look up, e.g. \"google\", \"amazon\", \"microsoft\". Required for Levels.fyi.", "default": "google" }, "role": { "title": "Role / job title", "type": "string", "description": "Job title, e.g. \"software engineer\", \"product manager\", \"data scientist\".", "default": "software engineer" }, "location": { "title": "Location (optional)", "type": "string", "description": "City filter for H1B data, e.g. \"new york\". Leave blank for all.", "default": "" }, "year": { "title": "H1B year (optional)", "minimum": 2015, "maximum": 2026, "type": "integer", "description": "Year of H1B wage filings to fetch. Defaults to last year." }, "sources": { "title": "Sources", "type": "array", "description": "Which compensation sources to scrape and merge.", "items": { "type": "string", "enum": [ "levels.fyi", "h1b" ], "enumTitles": [ "Levels.fyi", "H1B wage data (beta)" ] }, "default": [ "levels.fyi" ] }, "maxItemsPerSource": { "title": "Max results per source", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum salary records to collect from each source before merging and dedupe.", "default": 200 }, "dedupe": { "title": "Remove duplicate records", "type": "boolean", "description": "Drop duplicate salary records across sources.", "default": true } } }, "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 } } } } } } } } } } ```