# LinkedIn Jobs Scraper (`scrapers_lat/linkedin-jobs-scraper`) Actor
Extract public LinkedIn job postings by keyword and location worldwide. Scrape job title, company, location, posted date, job URL, company page, seniority, employment type, job function, industries and full description.
- **URL**: https://apify.com/scrapers\_lat/linkedin-jobs-scraper.md
- **Developed by:** [Michael Flores](https://apify.com/scrapers_lat) (community)
- **Categories:** Jobs, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $8.00 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
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
## LinkedIn Jobs Scraper
> Extract public job postings from LinkedIn by keyword and location across every country LinkedIn serves, with full descriptions and hiring criteria.
15 fields per record
Worldwide coverage
JSON / CSV / Excel output formats
Updated 2026-06-22
### What you get
Each record is one public LinkedIn job posting matched to your keyword and location search, ready to load into a spreadsheet, a hiring tracker or a talent pipeline.
- **imageUrl**: company logo image
- **title**: job title
- **company**: hiring company name
- **location**: job location as shown on the posting
- **remote**: true or false when you filter by workplace type, otherwise empty
- **postedAt**: date the job was posted in ISO format
- **jobId**: LinkedIn job posting identifier
- **url**: direct link to the public job posting
- **companyUrl**: link to the company page on LinkedIn
- **description\***: full job description text
- **seniority\***: seniority level required for the role
- **employmentType\***: full-time, part-time, contract and similar
- **jobFunction\***: job function category
- **industries\***: industries the role belongs to
- **applicants\***: applicant count when LinkedIn shows it
- **observedAt**: when this listing was last seen by the scraper
*\*These fields only appear when Fetch Job Details is set to true.*
### Who is it for
| Use case | Who benefits |
|---|---|
| Build a live feed of open roles for a niche or location | Recruiters and staffing agencies |
| Track competitor hiring and team growth signals | Sales and market researchers |
| Aggregate listings into a job board or newsletter | Job board and community operators |
| Monitor salary, seniority and demand trends over time | Talent and compensation analysts |
| Source leads from companies that are actively hiring | B2B and lead generation teams |
### Frequently Asked Questions
**Which countries and locations does this LinkedIn jobs scraper cover?**
It works with any location LinkedIn supports worldwide. You type a location such as United States, London or Remote and the scraper returns matching public postings for that area.
**How many job postings can I collect in one run?**
You control the volume with the Max Items setting. The scraper paginates through LinkedIn results automatically, so you can pull anywhere from a handful to thousands of listings depending on how many match your search.
**Can I filter by date posted, remote work or experience level?**
Yes. You can limit results to the past 24 hours, week or month, restrict to on-site, remote or hybrid roles, and target a specific experience level from internship through executive.
**What is the difference between running with and without job details?**
Without details you get the fast listing fields like title, company, location and posted date. With details enabled the scraper opens each posting to add the full description, seniority, employment type, job function, industries and applicant count.
**What happens when a field is missing on a posting?**
Missing values are returned as empty rather than guessed. For example the applicant count only appears when LinkedIn displays it, and the remote flag is only set when you apply a workplace type filter.
---
> This actor is an independent tool and has no affiliation with LinkedIn. It only accesses data that is publicly available on the platform. Use it in accordance with LinkedIn's terms of service.
# Actor input Schema
## `maxItems` (type: `integer`):
Maximum number of job postings to collect. Optional.
## `withDetails` (type: `boolean`):
When enabled, the actor opens each job posting to add the full description, seniority level, employment type, job function, industries and applicant count.
## `keywords` (type: `string`):
Job search keywords, for example "software engineer" or "product manager".
## `location` (type: `string`):
Location name to search in, for example "United States", "London" or "Remote".
## `timePosted` (type: `string`):
Filter by how recently the job was posted.
## `workplaceType` (type: `string`):
Filter by on-site, remote or hybrid roles.
## `experienceLevel` (type: `string`):
Filter by required experience level.
## `sortBy` (type: `string`):
Sort results by relevance or by most recent.
## `proxyConfiguration` (type: `object`):
Proxy settings. Enabled by default using Apify datacenter proxies for reliability.
## Actor input object example
```json
{
"maxItems": 10,
"withDetails": true,
"keywords": "software engineer",
"location": "United States",
"timePosted": "",
"workplaceType": "",
"experienceLevel": "",
"sortBy": "R",
"proxyConfiguration": {
"useApifyProxy": true
}
}
````
# Actor output Schema
## `results` (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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"maxItems": 10,
"keywords": "software engineer",
"location": "United States"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapers_lat/linkedin-jobs-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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"maxItems": 10,
"keywords": "software engineer",
"location": "United States",
}
# Run the Actor and wait for it to finish
run = client.actor("scrapers_lat/linkedin-jobs-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 '{
"maxItems": 10,
"keywords": "software engineer",
"location": "United States"
}' |
apify call scrapers_lat/linkedin-jobs-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapers_lat/linkedin-jobs-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LinkedIn Jobs Scraper",
"description": "Extract public LinkedIn job postings by keyword and location worldwide. Scrape job title, company, location, posted date, job URL, company page, seniority, employment type, job function, industries and full description.",
"version": "0.1",
"x-build-id": "OdubIiJsZqPRoQxyY"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapers_lat~linkedin-jobs-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapers_lat-linkedin-jobs-scraper",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapers_lat~linkedin-jobs-scraper/runs": {
"post": {
"operationId": "runs-sync-scrapers_lat-linkedin-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapers_lat~linkedin-jobs-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrapers_lat-linkedin-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"required": [
"keywords"
],
"properties": {
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 1000000,
"type": "integer",
"description": "Maximum number of job postings to collect. Optional."
},
"withDetails": {
"title": "Fetch Job Details",
"type": "boolean",
"description": "When enabled, the actor opens each job posting to add the full description, seniority level, employment type, job function, industries and applicant count.",
"default": true
},
"keywords": {
"title": "Keywords",
"type": "string",
"description": "Job search keywords, for example \"software engineer\" or \"product manager\"."
},
"location": {
"title": "Location",
"type": "string",
"description": "Location name to search in, for example \"United States\", \"London\" or \"Remote\"."
},
"timePosted": {
"title": "Date Posted",
"enum": [
"",
"r86400",
"r604800",
"r2592000"
],
"type": "string",
"description": "Filter by how recently the job was posted.",
"default": ""
},
"workplaceType": {
"title": "Workplace Type",
"enum": [
"",
"1",
"2",
"3"
],
"type": "string",
"description": "Filter by on-site, remote or hybrid roles.",
"default": ""
},
"experienceLevel": {
"title": "Experience Level",
"enum": [
"",
"1",
"2",
"3",
"4",
"5",
"6"
],
"type": "string",
"description": "Filter by required experience level.",
"default": ""
},
"sortBy": {
"title": "Sort By",
"enum": [
"R",
"DD"
],
"type": "string",
"description": "Sort results by relevance or by most recent.",
"default": "R"
},
"proxyConfiguration": {
"title": "Proxy Configuration",
"type": "object",
"description": "Proxy settings. Enabled by default using Apify datacenter proxies for reliability.",
"default": {
"useApifyProxy": true
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```