# LinkedIn Job Listing Scrapper (`zerobreak/linkedin-job-listing-scrapper`) Actor

Fast LinkedIn Job Listings Scraper that extracts real-time job posts by keywords, filters, or URLs. Ideal for recruiters, job seekers, and market analysts. Get full job details including title, company, location, skills, and description in bulk.

- **URL**: https://apify.com/zerobreak/linkedin-job-listing-scrapper.md
- **Developed by:** [ZeroBreak](https://apify.com/zerobreak) (community)
- **Categories:** Lead generation
- **Stats:** 104 total users, 2 monthly users, 90.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $9.99 / 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## LinkedIn Job Listings Scraper

Discover and extract job listings from LinkedIn using multiple search methods. Perfect for job market analysis, recruitment, and career research.

### Features

- ✅ Three powerful scraping methods
- ✅ Search by keywords with advanced filters
- ✅ Scrape specific job postings
- ✅ Discover jobs from search URLs
- ✅ Get complete job details
- ✅ Export in multiple formats

### Scraping Methods (Categories)

#### 1. Collect by URL

Scrape specific job postings when you have direct job URLs.

**Best for**: Tracking specific positions or following up on applications

#### 2. Discover by Keyword

Search for jobs using keywords, location, and filters.

**Best for**: Job market research, finding opportunities matching specific criteria

#### 3. Discover by URL

Discover jobs from LinkedIn job search result URLs.

**Best for**: Scraping search results, analyzing listings in bulk

### How It Works

**Two-Step Process:**

1. **Generate Snapshot ID**: Submit your search criteria or URLs
2. **Fetch Data**: After 30 seconds, retrieve all job listings

### Input

#### Required Fields

- **Action**: Generate Snapshot ID or Fetch Data
- **Category**: Choose your scraping method

#### Category-Specific Inputs

##### Collect by URL

- **Job URLs**: Direct LinkedIn job posting URLs (one per line)

##### Discover by Keyword

- **Keyword**: Job title or search term (e.g., "software engineer")
- **Location**: City, state, or "Remote"
- **Country**: Country code (e.g., "us", "uk")
- **Time Range**: "past-24-hours", "past-week", "past-month"
- **Job Type**: "Full-time", "Part-time", "Contract", etc.
- **Experience Level**: "Entry level", "Mid-Senior level", "Director", etc.
- **Remote Type**: "On-site", "Remote", "Hybrid"
- **Company**: Filter by specific company name
- **Location Radius**: Search radius in kilometers

##### Discover by URL

- **Job URLs**: LinkedIn job search result page URLs

### Input Examples

#### Example 1: Collect Specific Jobs

```json
{
  "action": "generate_snap_id",
  "category": "collect_by_url",
  "urls": "https://www.linkedin.com/jobs/view/3990815942/\nhttps://www.linkedin.com/jobs/view/3990815943/"
}
```

#### Example 2: Search by Keyword

```json
{
  "action": "generate_snap_id",
  "category": "discover_by_keyword",
  "keyword": "data scientist",
  "location": "San Francisco",
  "country": "us",
  "time_range": "past-week",
  "job_type": "Full-time",
  "remote": "Hybrid",
  "experience_level": "Mid-Senior level"
}
```

#### Example 3: From Search Results

```json
{
  "action": "generate_snap_id",
  "category": "discover_by_url",
  "urls": "https://www.linkedin.com/jobs/search/?keywords=python%20developer&location=Remote"
}
```

#### Example 4: Fetch Data (Wait 30 seconds!)

```json
{
  "action": "fetch_data",
  "category": "collect_by_url",
  "snap_id": "linkedin_job_listing_s_job789"
}
```

### Data Extracted

#### Job Details

- Job title
- Company name and LinkedIn URL
- Job location
- Employment type (Full-time, Part-time, etc.)
- Experience level required
- Job description (full text)
- Posted date
- Number of applicants
- Direct job application URL

#### Additional Info

- Required skills
- Salary range (when available)
- Benefits information
- Company size
- Industries

### Example Output

```json
{
  "title": "Senior Software Engineer",
  "company": "Microsoft",
  "company_url": "https://www.linkedin.com/company/microsoft/",
  "location": "Redmond, WA",
  "employment_type": "Full-time",
  "experience_level": "Mid-Senior level",
  "description": "We are seeking an experienced software engineer to join our cloud infrastructure team...",
  "posted_date": "2024-01-15",
  "applicants": 247,
  "job_url": "https://www.linkedin.com/jobs/view/3990815942/",
  "salary_range": "$140,000 - $200,000/year",
  "workplace_type": "Hybrid",
  "skills_required": [
    "Python",
    "AWS",
    "Kubernetes",
    "Docker"
  ]
}
```

### Step-by-Step Guide

#### Quick Start

1. **Choose Your Method**
   - Direct URLs → Use "Collect by URL"
   - Search criteria → Use "Discover by Keyword"
   - Search results → Use "Discover by URL"

2. **Start Scraping**
   - Select action: "Generate Snapshot ID"
   - Choose category
   - Fill in required fields
   - Run the actor
   - **Copy your snapshot ID**

3. **Wait 30 Seconds**
   Set a timer! This ensures all data is collected.

4. **Get Your Jobs**
   - Select action: "Fetch Data"
   - Select the same category
   - Enter your snapshot ID
   - Run to get results

### Important Notes

⚠️ **Category Must Match**
Use the same category when generating and fetching. If you generated with "collect_by_url", fetch with "collect_by_url".

⚠️ **30-Second Minimum Wait**
Fetching too early will cause errors. Wait at least 30 seconds!

⚠️ **Valid URLs Required**
For URL-based methods, use complete LinkedIn job URLs.

### Use Cases

#### Job Market Analysis

- Track job posting trends
- Analyze salary ranges by role
- Identify in-demand skills
- Monitor hiring activity

#### Recruitment & Talent

- Source candidates
- Competitive intelligence
- Job posting research
- Talent pool analysis

#### Career Planning

- Job search automation
- Salary research
- Skills gap analysis
- Market opportunity identification

#### Business Intelligence

- Competitor hiring tracking
- Market expansion research
- Industry growth indicators

### Tips for Best Results

#### Keyword Search

- Be specific but not too narrow
- Try variations (e.g., "ML Engineer" vs "Machine Learning Engineer")
- Use location for better targeting
- Recent time ranges get more active listings

#### URL Collection

- Copy complete URLs from job pages
- Avoid URLs with tracking parameters
- Use search result page URLs for bulk discovery

#### Filters

- Combine multiple filters for precision
- Location radius helps for commutable jobs
- Experience level filters reduce noise

### Common Questions

**Q: How many jobs can I scrape at once?**
A: No hard limit, but recommend batches of 100-200 for optimal performance.

**Q: Can I get historical job data?**
A: Only currently active postings are available.

**Q: What if a job has been removed?**
A: You may get limited data or a note that the job is no longer available.

**Q: Can I scrape jobs from all countries?**
A: Yes, from any LinkedIn-supported country.

**Q: How accurate is the salary data?**
A: Extracted when available on the posting, accuracy depends on employer disclosure.

### Keyword Search Filters Explained

| Filter | Options | Example |
|--------|---------|---------|
| Time Range | past-24-hours, past-week, past-month | past-week |
| Job Type | Full-time, Part-time, Contract, Temporary, Internship | Full-time |
| Experience Level | Internship, Entry level, Associate, Mid-Senior level, Director, Executive | Mid-Senior level |
| Remote | On-site, Remote, Hybrid | Remote |

### Data Export

Download your results in:

- **JSON**: Developer-friendly format
- **CSV**: Spreadsheet compatible
- **Excel**: Ready for analysis
- **XML**: System integration

### Pricing

- Charged per job listing scraped
- All categories same price
- Snapshot IDs valid 24 hours

### Troubleshooting

#### No results found

- Try broader keywords
- Remove location restrictions
- Expand time range
- Check spelling

#### Partial data

- Some employers limit visible info
- Wait longer before fetching (up to 60 seconds)

#### Category mismatch error

- Use same category for generate and fetch
- Double-check your input

### Best Practices

1. **Start Small**: Test with a few jobs first
2. **Use Filters**: Narrow results for relevance
3. **Check URLs**: Verify links before bulk scraping
4. **Save Snapshot IDs**: Keep them for 24 hours
5. **Export Regularly**: Download data from dataset

### Support

Having trouble?

- Review the run logs for detailed errors
- Verify your input format
- Check example inputs above
- Contact Apify support

***

**Find your next opportunity or insight! 💼**

# Actor input Schema

## `action` (type: `string`):

Choose whether to generate a snapshot ID or fetch data

## `category` (type: `string`):

Type of job listing scraping method

## `snap_id` (type: `string`):

Snapshot ID to fetch data (required for fetch_data action). ⚠️ Wait 30 seconds after generating.

## `urls` (type: `string`):

LinkedIn job URLs, ONE URL PER LINE (for collect_by_url or discover_by_url categories)

## `keyword` (type: `string`):

Job search keyword (for discover_by_keyword category)

## `location` (type: `string`):

Job location (for discover_by_keyword)

## `country` (type: `string`):

Country code (for discover_by_keyword)

## `time_range` (type: `string`):

When job was posted

## `job_type` (type: `string`):

Type of employment

## `experience_level` (type: `string`):

Required experience level

## `remote` (type: `string`):

Remote work type

## `company` (type: `string`):

Filter by company

## `location_radius` (type: `string`):

Search radius in kilometers

## Actor input object example

```json
{
  "action": "generate_snap_id",
  "category": "collect_by_url",
  "snap_id": "linkedin_job_listing_s_job789",
  "urls": "https://www.linkedin.com/jobs/view/1234567890/\nhttps://www.linkedin.com/jobs/view/0987654321/",
  "keyword": "software engineer",
  "location": "San Francisco",
  "country": "us",
  "time_range": "past-week",
  "job_type": "Full-time",
  "experience_level": "Mid-Senior level",
  "remote": "Remote",
  "company": "Google",
  "location_radius": "50"
}
```

# Actor output Schema

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

// Prepare Actor input
const input = {
    "action": "generate_snap_id",
    "category": "collect_by_url",
    "snap_id": "",
    "urls": "https://www.linkedin.com/jobs/view/software-engineer-at-pave-4310512612/",
    "keyword": "software engineer",
    "location": "",
    "country": "",
    "time_range": "",
    "job_type": "",
    "experience_level": "",
    "remote": "",
    "company": "",
    "location_radius": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("zerobreak/linkedin-job-listing-scrapper").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 = {
    "action": "generate_snap_id",
    "category": "collect_by_url",
    "snap_id": "",
    "urls": "https://www.linkedin.com/jobs/view/software-engineer-at-pave-4310512612/",
    "keyword": "software engineer",
    "location": "",
    "country": "",
    "time_range": "",
    "job_type": "",
    "experience_level": "",
    "remote": "",
    "company": "",
    "location_radius": "",
}

# Run the Actor and wait for it to finish
run = client.actor("zerobreak/linkedin-job-listing-scrapper").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 '{
  "action": "generate_snap_id",
  "category": "collect_by_url",
  "snap_id": "",
  "urls": "https://www.linkedin.com/jobs/view/software-engineer-at-pave-4310512612/",
  "keyword": "software engineer",
  "location": "",
  "country": "",
  "time_range": "",
  "job_type": "",
  "experience_level": "",
  "remote": "",
  "company": "",
  "location_radius": ""
}' |
apify call zerobreak/linkedin-job-listing-scrapper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,zerobreak/linkedin-job-listing-scrapper"
        }
    }
}
```

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/V8iUxqPefsi0pe5gQ/builds/RkO9ciObcEKaUavQ5/openapi.json