# 🛂 H-1B Visa Salary & Sponsor Database (`inexhaustible_glass/h1b-salary-database`) Actor Search every H-1B visa filing (LCA) by employer, job title, city & year and get the EXACT base salary filed with the US Dept of Labor, worksite, filing & start dates + salary stats (avg/median/range). For job seekers, recruiters, immigration lawyers, HR. Free, no key, no proxy. - **URL**: https://apify.com/inexhaustible\_glass/h1b-salary-database.md - **Developed by:** [Hitman studio](https://apify.com/inexhaustible_glass) (community) - **Categories:** Business, Lead generation, Developer tools - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 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 ## 🛂 H-1B Visa Salary & Sponsor Database > **What does any company really pay?** Search every public H-1B visa filing by employer, job > title, city and year and get the **exact base salary** the company filed with the US Department > of Labor — plus worksite, dates and salary statistics. No key, no proxy, never blocked. Every employer that sponsors an H-1B worker must file a Labor Condition Application (LCA) that becomes **public record**. This actor turns that into a clean, searchable salary database. --- ### 👥 Who needs this | You are a… | You get… | |---|---| | 🧑‍💻 **Job seeker / candidate** | Real salaries by company & role — know your worth before you negotiate | | 🔎 **Recruiter / sourcer** | Which companies sponsor visas + comp benchmarks | | ⚖️ **Immigration lawyer** | Precedent salaries & an employer's filing history | | 💼 **HR / compensation team** | Salary benchmarking by title & city | | 📊 **Jobs / salary website** | Bulk, normalized comp data feed | ### 🆚 Better than a raw dump - ✅ **Exact filed base salary** parsed to a number (sortable / filterable) - ✅ **City & State split out** of the worksite - ✅ **ISO dates** (submit & start) - ✅ **Salary statistics** per search — count, min, max, **average & median** - ✅ **Multi-year** in one run + salary-range filter --- ### 🚀 How to use 1. Add an **employer** and/or a **job title** and/or a **city**. 2. Pick the **year(s)** → press **Start** → download CSV / Excel / JSON. #### Example input ```json { "employer": "Google", "jobTitle": "software engineer", "years": ["2024"], "minSalary": 150000, "maxResults": 1000 } ```` 👉 *"Every $150k+ software-engineer salary Google filed in 2024."* #### More examples - `{ "jobTitle": "data scientist", "city": "New York", "years": ["2024","2023"] }` - `{ "employer": "Amazon", "years": ["2024"] }` — every Amazon filing + salary stats - `{ "jobTitle": "product manager", "minSalary": 200000 }` — top-paying PM roles *** ### 📦 Output (per filing) `employer`, `job_title`, `base_salary`, `salary_period`, `location`, `city`, `state`, `submit_date`, `start_date`, `filing_year`, `visa_type`, `source_url`. A **SUMMARY** (count, min, max, average, median salary) is saved to the run's key-value store. *** ### ℹ️ Notes - **Source:** public DOL LCA disclosure data (h1bdata.info) — free, no key, no proxy. - Shows the **filed base wage** (offered salary). Like all H-1B data it excludes stock, bonuses and other compensation, which are not part of the public filing. - Tip: search a role by several common titles — employers title the same job differently. *** *Keywords: H-1B, H1B, visa salary, LCA, labor condition application, visa sponsor, prevailing wage, salary database, comp data, immigration, software engineer salary, tech salaries, DOL disclosure, visa sponsorship companies, salary benchmark.* # Actor input Schema ## `employer` (type: `string`): Company name (partial is fine). Examples: 'Google', 'Amazon', 'Meta', 'Microsoft'. Leave empty to search by job title or city instead. ## `jobTitle` (type: `string`): Role to search. Examples: 'software engineer', 'data scientist', 'product manager', 'machine learning engineer'. ## `city` (type: `string`): Worksite city. Examples: 'San Francisco', 'New York', 'Seattle', 'Austin'. ## `years` (type: `array`): Which filing year(s) to pull. Pick one or several. Data goes back to 2012. ## `minSalary` (type: `integer`): Only filings paying at least this much. Example: 150000 for $150k+ roles. ## `maxSalary` (type: `integer`): Only filings paying at most this much. Leave empty for no upper limit. ## `includeStats` (type: `boolean`): Compute count, min, max, average & median salary for the search and save it to the run's SUMMARY (key-value store). ## `maxResults` (type: `integer`): Cap how many filings to return. ## Actor input object example ```json { "employer": "Google", "jobTitle": "data scientist", "city": "San Francisco", "years": [ "2024" ], "includeStats": true, "maxResults": 1000 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "employer": "Google", "years": [ "2024" ] }; // Run the Actor and wait for it to finish const run = await client.actor("inexhaustible_glass/h1b-salary-database").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "employer": "Google", "years": ["2024"], } # Run the Actor and wait for it to finish run = client.actor("inexhaustible_glass/h1b-salary-database").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 '{ "employer": "Google", "years": [ "2024" ] }' | apify call inexhaustible_glass/h1b-salary-database --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=inexhaustible_glass/h1b-salary-database", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🛂 H-1B Visa Salary & Sponsor Database", "description": "Search every H-1B visa filing (LCA) by employer, job title, city & year and get the EXACT base salary filed with the US Dept of Labor, worksite, filing & start dates + salary stats (avg/median/range). For job seekers, recruiters, immigration lawyers, HR. Free, no key, no proxy.", "version": "1.0", "x-build-id": "jRJRAqg7Xwun6jun8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/inexhaustible_glass~h1b-salary-database/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-inexhaustible_glass-h1b-salary-database", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/inexhaustible_glass~h1b-salary-database/runs": { "post": { "operationId": "runs-sync-inexhaustible_glass-h1b-salary-database", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/inexhaustible_glass~h1b-salary-database/run-sync": { "post": { "operationId": "run-sync-inexhaustible_glass-h1b-salary-database", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "employer": { "title": "🏢 Employer / company", "type": "string", "description": "Company name (partial is fine). Examples: 'Google', 'Amazon', 'Meta', 'Microsoft'. Leave empty to search by job title or city instead." }, "jobTitle": { "title": "💼 Job title", "type": "string", "description": "Role to search. Examples: 'software engineer', 'data scientist', 'product manager', 'machine learning engineer'." }, "city": { "title": "📍 City", "type": "string", "description": "Worksite city. Examples: 'San Francisco', 'New York', 'Seattle', 'Austin'." }, "years": { "title": "📅 Year(s)", "type": "array", "description": "Which filing year(s) to pull. Pick one or several. Data goes back to 2012.", "items": { "type": "string", "enum": [ "2026", "2025", "2024", "2023", "2022", "2021", "2020", "2019", "2018", "2017", "2016", "2015", "2014", "2013", "2012" ], "enumTitles": [ "2026", "2025", "2024", "2023", "2022", "2021", "2020", "2019", "2018", "2017", "2016", "2015", "2014", "2013", "2012" ] }, "default": [ "2024" ] }, "minSalary": { "title": "💰 Minimum salary ($)", "minimum": 0, "type": "integer", "description": "Only filings paying at least this much. Example: 150000 for $150k+ roles." }, "maxSalary": { "title": "💵 Maximum salary ($)", "minimum": 0, "type": "integer", "description": "Only filings paying at most this much. Leave empty for no upper limit." }, "includeStats": { "title": "📊 Include salary statistics", "type": "boolean", "description": "Compute count, min, max, average & median salary for the search and save it to the run's SUMMARY (key-value store).", "default": true }, "maxResults": { "title": "🔢 Max results", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Cap how many filings to return.", "default": 1000 } } }, "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 } } } } } } } } } } ```