# H-1B Salary Data: Employer, Job Title and Base Pay (`scrapemint/h1b-salary-data`) Actor

Base salaries US employers filed with the Department of Labor to sponsor H-1B workers: employer, job title, base pay, work location and filing dates, plus median and percentile summaries. Keyless JSON, no browser and no proxy.

- **URL**: https://apify.com/scrapemint/h1b-salary-data.md
- **Developed by:** [Ken M](https://apify.com/scrapemint) (community)
- **Categories:** Jobs, Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$5.00 / 1,000 salary rows

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 Salary Data

Real base salaries that US employers filed with the Department of Labor to sponsor H-1B workers. Search by company, job title or city and get the actual pay figures, plus the median and percentiles for whatever you searched.

These are not self reported estimates. Every number here comes from a Labor Condition Application the employer signed, so the figures are exact and legally attested.

No login, no API key, no proxy.

### What you get

One row per filing:

```json
{
  "employer": "STRIPE INC",
  "jobTitle": "ACCOUNT EXECUTIVE",
  "baseSalary": 99424,
  "baseSalaryRaw": "99,424",
  "location": "NEW YORK, NY",
  "city": "NEW YORK",
  "state": "NY",
  "submitDate": "2025-06-03",
  "startDate": "2025-10-01",
  "filingYear": 2025
}
```

Plus one summary row per run, unless you turn it off:

```json
{
  "type": "summary",
  "employers": ["stripe"],
  "years": ["2025"],
  "filingsMatched": 268,
  "salariesParsed": 268,
  "minSalary": 81474,
  "p25Salary": 145000,
  "medianSalary": 189000,
  "p75Salary": 232000,
  "maxSalary": 405000
}
```

A salary that is blank or unparseable comes back as `null`, never as `0`, so a missing wage can never be mistaken for a real filed one. The same holds for the summary: if nothing parsed, every statistic is `null`.

### Input

| Field | Description |
| --- | --- |
| `employers` | Company names, e.g. `["stripe","nvidia"]`. Partial names work |
| `jobTitle` | Filter by job title, e.g. `"data scientist"` |
| `city` | Filter by work location city |
| `years` | Filing years, e.g. `["2024","2025"]`. Empty uses the most recent year with data |
| `includeSummary` | Add the median and percentile row (default true) |
| `sortBy` | `salary` (default) or `submitDate` |
| `maxRows` | Salary rows returned after sorting (default 200) |

At least one of `employers`, `jobTitle` or `city` is required.

### Examples

Everything one company filed, with percentiles:

```json
{ "employers": ["stripe"], "years": ["2025"] }
```

What data scientists are paid across all employers:

```json
{ "jobTitle": "data scientist", "years": ["2025"], "maxRows": 500 }
```

One role at one company over several years:

```json
{ "employers": ["nvidia"], "jobTitle": "software engineer", "years": ["2023","2024","2025"] }
```

### Notes on the data

Filings are disclosed on a lag, so the current year is often empty or thin early on. Leaving `years` empty walks back to the most recent year that actually has filings for your search.

There is no "all years" option upstream, so each year you ask for is a separate fetch. Asking for three years costs three requests per employer.

A filing is a request to sponsor at a stated wage. It is strong evidence of what a role pays, but it is not proof a person was hired at that number.

### Who it's for

Candidates benchmarking an offer, recruiters and compensation analysts pricing roles, and immigration teams checking what comparable employers filed. Pairs with the **LinkedIn Jobs Scraper** and **Company Job Openings Scraper** for the openings themselves.

### Pricing

Pay per salary row. The first 2 rows of every run are free so you can validate the output before you pay.

# Actor input Schema

## `employers` (type: `array`):

Company names to look up, e.g. \["stripe","nvidia"]. Partial names work. Leave empty to search by job title or city across all employers.

## `jobTitle` (type: `string`):

Filter by job title, e.g. "data scientist". Combines with employers and city.

## `city` (type: `string`):

Filter by work location city, e.g. "seattle".

## `years` (type: `array`):

Years to pull, e.g. \[2024, 2025]. Leave empty to use the most recent year that has filings. Note the site has no "all years" option, so each year is fetched separately.

## `includeSummary` (type: `boolean`):

Add one row with the median, 25th and 75th percentile, minimum and maximum base salary for everything matched.

## `sortBy` (type: `string`):

Order the salary rows.

## `maxRows` (type: `integer`):

Salary rows to return after sorting. The summary row does not count towards this.

## Actor input object example

```json
{
  "employers": [
    "stripe",
    "nvidia"
  ],
  "jobTitle": "software engineer",
  "city": "seattle",
  "years": [
    "2024",
    "2025"
  ],
  "includeSummary": true,
  "sortBy": "salary",
  "maxRows": 200
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "employers": [
        "stripe"
    ],
    "years": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapemint/h1b-salary-data").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "employers": ["stripe"],
    "years": [],
}

# Run the Actor and wait for it to finish
run = client.actor("scrapemint/h1b-salary-data").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "employers": [
    "stripe"
  ],
  "years": []
}' |
apify call scrapemint/h1b-salary-data --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapemint/h1b-salary-data"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/E04LFlbwA6m8lSLid/builds/4hyeTJBu5L9GuBJ2L/openapi.json