# Linkedin\_SuccessfulJobScraper (`optative_sweater/linkedin-successfuljobscraper`) Actor

🟢 $0.99/1000 results 🟢Scrape LinkedIn job postings by keyword search. Extracts job title, company, location, description, duties, requirements, posted date, and applicant count into a structured Job Listings table for an organized overview of the job market!

- **URL**: https://apify.com/optative\_sweater/linkedin-successfuljobscraper.md
- **Developed by:** [Sss Stanley](https://apify.com/optative_sweater) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 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/actors/running/actors-in-store.md#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 Scraper

Scrape LinkedIn job postings by keyword search. Extracts job title, company, location, description, duties, requirements, posted date, and applicant count into a structured **Job Listings** table for an organized overview of the job market.

### Features

- **Keyword-based search** — Enter one or more keywords, each runs a separate LinkedIn search
- **Filters** — Location, posted within, workplace type (on-site / remote / hybrid), experience level
- **Parallel extraction** — Up to 20 concurrent job page fetches for fast results
- **Smart description splitting** — Automatically separates job duties and requirements into dedicated columns
- **Ordered Excel/CSV export** — Download `jobs.xls` or `jobs.csv` from the Key-Value Store with columns in the correct order

### Output columns

| Column | Description |
|---|---|
| No. | Sequential row number |
| Keyword | The search keyword that found this job |
| Company | Hiring company name |
| Description | Full job description text |
| Title | Job title |
| Duties | Job responsibilities (extracted from description) |
| Requirements | Qualifications and requirements (extracted from description) |
| Location | Job location |
| Job ID | LinkedIn job posting ID |
| URL | Direct link to the job posting |
| Posted | When the job was posted (e.g. "2 days ago") |
| Applicants | Number of applicants or people who clicked apply |

### How to use

1. Enter your search **keywords** (one per line or comma-separated)
2. Set optional filters: location, posted within, workplace type, experience level
3. Run the actor
4. View results in the **Job Listings** table view under the Dataset tab for an organized layout
5. Download `jobs.xls` or `jobs.csv` from **Storage → Key-value store** for correct column ordering

> 💡 **Tip: Pair with AI for smarter filtering.** After extraction, feed the results into ChatGPT, Claude, or any AI tool to filter jobs by your resume, skills, or specific criteria — e.g. *"From these jobs, shortlist the ones that match a background in financial due diligence and require less than 5 years of experience."*

### Notes

ℹ️ This tool scrapes jobs from the public LinkedIn guest API, where available results may differ from a logged-in LinkedIn search. For best results, verify your keywords by searching in an incognito window: https://www.linkedin.com/jobs/search

ℹ️ Providing your `li_at` cookie gives access to more complete job data (posted date, applicant count, etc.) that may not appear on guest pages.

ℹ️ Proxy configuration uses residential proxies by default for reliable access to LinkedIn.

# Actor input Schema

## `keywords` (type: `string`):

Search keywords, one per line or comma-separated. Each keyword runs a separate search query.

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

Location filter (e.g. "Hong Kong", "London", "New York").

## `postedWithin` (type: `string`):

Time range for when the job was posted.

## `workplaceType` (type: `string`):

Filter by workplace arrangement.

## `experienceLevel` (type: `string`):

Filter by seniority.

## `maxPerKeyword` (type: `integer`):

Maximum number of job IDs to collect per keyword before moving to the next.

## `maxTotal` (type: `integer`):

Overall cap across all keywords.

## `delaySeconds` (type: `integer`):

Pause between HTTP requests during search phase. Keep at 2+ to avoid rate limiting.

## `concurrency` (type: `integer`):

How many jobs to fetch in parallel. Higher = faster but more likely to get rate-limited. 5-10 is a good balance.

## `proxy` (type: `object`):

Apify proxy settings. Residential proxies recommended for LinkedIn.

## `cookie` (type: `string`):

Your LinkedIn session cookie (li\_at value). Required for logged-in access. Find it in browser DevTools → Application → Cookies → linkedin.com → li\_at.

## Actor input object example

```json
{
  "keywords": "financial due diligence\nasset management",
  "location": "",
  "postedWithin": "r604800",
  "workplaceType": "",
  "experienceLevel": "",
  "maxPerKeyword": 75,
  "maxTotal": 200,
  "delaySeconds": 2,
  "concurrency": 20,
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

## `jobs` (type: `string`):

All scraped job postings, one item per posting.

# 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 = {
    "keywords": `financial due diligence
asset management`
};

// Run the Actor and wait for it to finish
const run = await client.actor("optative_sweater/linkedin-successfuljobscraper").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 = { "keywords": """financial due diligence
asset management""" }

# Run the Actor and wait for it to finish
run = client.actor("optative_sweater/linkedin-successfuljobscraper").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 '{
  "keywords": "financial due diligence\\nasset management"
}' |
apify call optative_sweater/linkedin-successfuljobscraper --silent --output-dataset

```

## MCP server setup

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

```

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/Y19fJdVfmmd5TlPwY/builds/JTxDtRwmxyvbAyXA0/openapi.json