# LinkedIn Job Detail Scraper (`good-apis/linkedin-job-detail-scraper`) Actor

Full detail for LinkedIn jobs by URL or id: description, seniority, employment type, applicants, salary. No login. $0.90/1k.

- **URL**: https://apify.com/good-apis/linkedin-job-detail-scraper.md
- **Developed by:** [Danny](https://apify.com/good-apis) (community)
- **Categories:** Jobs, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.90 / 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 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

## LinkedIn Job Detail Scraper

Fetch the full detail for LinkedIn jobs by URL or id: description, seniority, employment type, function, industries, applicants, salary (when the employer posted it). Returns clean JSON. **$0.90 / 1,000 jobs.**

> Public, logged-out data only — no login, no cookies, no account.

### Input

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `job_urls_or_ids` | array | Yes | LinkedIn job URLs or numeric ids. |

### Example Input

```json
{
  "job_urls_or_ids": [
    "https://www.linkedin.com/jobs/view/4438850133",
    "4406548495"
  ]
}
```

### Output

Each dataset item is one job:

```json
{
  "job_id": "4438850133",
  "title": "...",
  "company_name": "...",
  "location": "...",
  "description_text": "...",
  "seniority_level": "...",
  "employment_type": "...",
  "job_function": "...",
  "industries": "...",
  "applicants_count": "...",
  "salary": "...",
  "apply_url": "...",
  "posted_at": "..."
}
```

#### Fields

| Field | Description |
|-------|-------------|
| `job_id` | LinkedIn job id |
| `title` | Job title |
| `company_name` | Employer |
| `location` | Job location |
| `description_text` | Full job description (plain text) |
| `seniority_level` | e.g. Mid-Senior level |
| `employment_type` | e.g. Full-time |
| `job_function` | e.g. Engineering |
| `industries` | e.g. Software Development |
| `applicants_count` | e.g. 'Over 200 applicants' (variable-fill) |
| `salary` | Comp string when the employer posted it (often null) |
| `apply_url` | Off-site apply URL when present |
| `posted_at` | ISO date posted |

### Pagination

Handled server-side — the actor returns the full result set for your input in one run (bounded by
`max_results` where applicable). LinkedIn's logged-out feed caps at ~500-530 unique jobs per query.

### Python Client

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")
run = client.actor("good-apis/linkedin-job-detail-scraper").call(run_input={
  "job_urls_or_ids": [
    "https://www.linkedin.com/jobs/view/4438850133",
    "4406548495"
  ]
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)
```

### Node.js Client

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('good-apis/linkedin-job-detail-scraper').call({
  "job_urls_or_ids": [
    "https://www.linkedin.com/jobs/view/4438850133",
    "4406548495"
  ]
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) console.log(item);
```

Install the client with `npm install apify-client`.

### FAQ

**What input does it take?** A list of LinkedIn job URLs or bare numeric ids in `job_urls_or_ids`.

**Why is salary/skills sometimes null?** LinkedIn's logged-out pages only expose salary when the employer stamped it, and never expose structured skills/benefits — those are reported null rather than invented.

**What is one billed result?** One returned job detail = one `result` event at $0.90 / 1,000 jobs.

# Actor input Schema

## `job_urls_or_ids` (type: `array`):

LinkedIn job URLs or numeric ids.

## Actor input object example

```json
{
  "job_urls_or_ids": [
    "https://www.linkedin.com/jobs/view/4438850133"
  ]
}
```

# Actor output Schema

## `results` (type: `string`):

Scraped jobs; each dataset item is one job.

# 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 = {
    "job_urls_or_ids": [
        "https://www.linkedin.com/jobs/view/4438850133"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("good-apis/linkedin-job-detail-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "job_urls_or_ids": ["https://www.linkedin.com/jobs/view/4438850133"] }

# Run the Actor and wait for it to finish
run = client.actor("good-apis/linkedin-job-detail-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 '{
  "job_urls_or_ids": [
    "https://www.linkedin.com/jobs/view/4438850133"
  ]
}' |
apify call good-apis/linkedin-job-detail-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=good-apis/linkedin-job-detail-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/bRMfknUAx7Ijg1Txm/builds/K03Jt7fZnSeoepRG1/openapi.json