# LinkedIn Jobs Scraper (`xtracto/linkedin-jobs-scraper`) Actor
Search public LinkedIn job postings by keyword + location and get structured listings — title, company, location, posted date and job link — with an option to pull the full job description, seniority, employment type, applicant count and salary. Public job postings only; no login.
- **URL**: https://apify.com/xtracto/linkedin-jobs-scraper.md
- **Developed by:** [Farhan Febrian Nauval](https://apify.com/xtracto) (community)
- **Categories:** Jobs, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $0.99 / 1,000 results
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
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/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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
Search **public LinkedIn job postings** by keyword and location and get clean, structured results — job title, company, location, posting date, and a direct link — with an option to pull the **full job description**, plus **seniority, employment type, applicant count, and salary**. No account, no login, no API key.
> **Public job postings only.** This actor collects public job listings — the kind anyone can browse without signing in. It does **not** touch personal profiles, contacts, emails, or anything behind a login.
### Why use this actor
- **Keyword + location search**, exactly like searching jobs on LinkedIn yourself.
- **Salary data when it's shown** — many postings include the pay range (e.g. `$200,000.00/yr - $300,000.00/yr`), captured verbatim.
- **Applicant count** — see how many people have applied (e.g. `87 applicants`, `Over 200 applicants`) to gauge competition.
- **Full job descriptions** on demand — flip one switch to add the complete description, seniority level, employment type, job function, and industries for every job.
- **Filter your search** — remote-only, jobs from a specific company, or only jobs posted in the last day / week / month.
- **Clean JSON output**, ready for spreadsheets, databases, or your own pipelines.
- **Nothing fails silently** — if a search returns nothing, you get a clear reason, not an empty file.
### How it works
1. You enter search keywords and a location (and optionally: remote-only, a company, or a date filter).
2. The actor runs that search on LinkedIn and reads the public job listings.
3. Optionally, it opens each job to add the full description, seniority, employment type, applicant count, and salary.
4. Results stream into your dataset, ready to download as JSON, CSV, or Excel.
You don't manage any of the fetching, retries, or blocking — just describe the search you want.
### How many results you get per search
LinkedIn's public job search returns results in pages of about 10, up to roughly **1000 jobs per search**. The actor pages through automatically and stops cleanly when it reaches the end (or your `maxItems` cap). To cover a larger pool, split one broad search into several focused ones — for example by narrower keywords, a tighter location, or the date filter.
### Input
```json
{
"keywords": "software engineer",
"location": "United States",
"datePosted": "pastWeek",
"remote": false,
"fetchDetail": true,
"maxItems": 50,
"maxConcurrency": 4,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": ["RESIDENTIAL"]
}
}
````
| Field | Type | Description |
|---|---|---|
| `keywords` | string | Search keywords — job title, skill, or company. Example: `software engineer`. |
| `location` | string | City, state, country, or region. Example: `United States`, `London`, `Remote`. Leave empty to search everywhere. |
| `companyId` | string | Optional. Show only jobs from one company (its LinkedIn numeric company ID, from the company page URL). |
| `datePosted` | string | `any` (default), `past24h`, `pastWeek`, or `pastMonth`. |
| `remote` | boolean | If `true`, only return jobs marked as remote. Default `false`. |
| `fetchDetail` | boolean | If `true`, opens each job to add the full description, seniority, employment type, job function, industries, applicant count, and salary. Slower but much richer. Default `false`. |
| `maxItems` | integer | Overall cap on jobs returned. `0` = all available (up to ~1000). Default `50`. |
| `maxConcurrency` | integer | How many job pages to open in parallel when `fetchDetail` is on. Default `4`. |
| `proxyConfiguration` | object | Residential proxy strongly recommended. Defaults to the United States if no country is set. |
### Output
One record per job. Below is a **real record** from the search `software engineer` in `United States` — with `fetchDetail` off:
```json
{
"_input": "software engineer @ United States",
"_source": "S1-guest-search",
"_scrapedAt": "2026-07-10T17:44:29Z",
"recordType": "JOB",
"jobId": "4438850133",
"title": "Software Engineer (All Levels)",
"company": "Blossom",
"companyLinkedinUrl": "https://www.linkedin.com/company/join-blossom-health",
"location": "New York, NY",
"postedDate": "2026-07-09",
"postedText": "18 hours ago",
"salaryText": null,
"jobUrl": "https://www.linkedin.com/jobs/view/software-engineer-all-levels-at-blossom-4438850133",
"searchKeywords": "software engineer",
"searchLocation": "United States",
"searchCompanyId": null,
"searchDatePosted": "any",
"searchRemote": false
}
```
When `fetchDetail` is `true`, each record also gets the full job content. Here is a **real detail record** — note the **salary** and **applicant count**:
```json
{
"_source": "S1-guest-search+S2-detail",
"jobId": "4434251228",
"title": "Systems Engineer",
"company": "Blacksmith",
"companyLinkedinUrl": "https://www.linkedin.com/company/blacksmithrun",
"location": "New York, NY",
"postedDate": "2026-06-27",
"jobUrl": "https://www.linkedin.com/jobs/view/systems-engineer-at-blacksmith-4434251228",
"seniority": "Not Applicable",
"employmentType": "Full-time",
"jobFunction": "Information Technology",
"industries": "Software Development",
"applicantCount": "87 applicants",
"salary": "$200,000.00/yr - $300,000.00/yr",
"descriptionText": "About Blacksmith\nWe started by building infrastructure to run CI workloads really fast. Our first product helps companies run GitHub Actions substantially faster and cheaper by owning and operating our own global fleet of bare-metal machines rather than rentin... (full text continues)",
"descriptionHtml": " ...
",
"jobCriteria": {
"Seniority level": "Not Applicable",
"Employment type": "Full-time",
"Job function": "Information Technology",
"Industries": "Software Development"
}
}
```
#### Output fields
| Field | Type | Description |
|---|---|---|
| `_input` | string | The search that produced this record. |
| `_source` | string | Where the data came from — `S1-guest-search` (search) or `S1-guest-search+S2-detail` (search + full detail). |
| `_scrapedAt` | string | UTC timestamp when the record was collected. |
| `recordType` | string | Always `JOB`. |
| `jobId` | string | LinkedIn's unique job posting ID. |
| `title` | string | Job title. |
| `company` | string | Employer name. |
| `companyLinkedinUrl` | string | Link to the company's LinkedIn page. |
| `location` | string | Job location as shown. |
| `postedDate` | string | Posting date (`YYYY-MM-DD`). |
| `postedText` | string | Human-readable posting age, e.g. `18 hours ago`. |
| `salaryText` | string | Salary snippet shown on the search card, when present. |
| `jobUrl` | string | Direct link to the job posting. |
| `searchKeywords` / `searchLocation` | string | The search inputs, echoed for traceability. |
| `searchCompanyId` / `searchDatePosted` / `searchRemote` | mixed | The filters used for this run. |
| `seniority` | string | Seniority level, e.g. `Mid-Senior level`. *(only with `fetchDetail`)* |
| `employmentType` | string | e.g. `Full-time`, `Contract`. *(only with `fetchDetail`)* |
| `jobFunction` | string | Job function, e.g. `Engineering and Information Technology`. *(only with `fetchDetail`)* |
| `industries` | string | Industry, e.g. `Software Development`. *(only with `fetchDetail`)* |
| `applicantCount` | string | Number of applicants, e.g. `87 applicants` or `Over 200 applicants`. *(only with `fetchDetail`)* |
| `salary` | string | Salary range from the job page, e.g. `$200,000.00/yr - $300,000.00/yr`, when shown. *(only with `fetchDetail`)* |
| `descriptionText` | string | Full job description as plain text. *(only with `fetchDetail`)* |
| `descriptionHtml` | string | Full job description with original formatting. *(only with `fetchDetail`)* |
| `jobCriteria` | object | All job-criteria pairs exactly as shown on the posting. *(only with `fetchDetail`)* |
Searches that return nothing produce a record with an `_error` field instead of job data — so a run never fails silently.
### Notes
- **Salary and applicant count appear only with `fetchDetail` on**, and only when the posting itself shows them (salary is most common for postings in locations with pay-transparency rules).
- **Each search returns up to ~1000 jobs.** For a bigger pool, split into several focused searches (narrower keywords, tighter location, or the date filter).
- **Public job postings only** — no login, no personal data.
- **Residential proxy is recommended** for reliable results at scale.
# Actor input Schema
## `keywords` (type: `string`):
Job title, skill or keyword to search for. Example: software engineer, product manager, registered nurse.
## `location` (type: `string`):
City, state, country or region to search in. Example: United States, London, Remote, New York.
## `companyId` (type: `string`):
Show only jobs from one company. This is the company's LinkedIn numeric ID (found in the company page URL). Leave empty to search all companies.
## `datePosted` (type: `string`):
Only return jobs posted within this window.
## `remote` (type: `boolean`):
If on, only return jobs marked as remote.
## `fetchDetail` (type: `boolean`):
If on, the actor opens each job to add the full description, seniority level, employment type, job function, industries, applicant count and salary (when shown). Slower but much richer.
## `maxItems` (type: `integer`):
Overall cap on jobs returned for this run. 0 = all available (LinkedIn shows up to about 1000 results per search).
## `maxConcurrency` (type: `integer`):
How many job pages to open in parallel when "Fetch full job details" is on. Keep moderate to stay reliable.
## `proxyConfiguration` (type: `object`):
Residential proxy is strongly recommended. If you don't set a country here, the actor defaults to the United States.
## Actor input object example
```json
{
"keywords": "software engineer",
"location": "United States",
"datePosted": "any",
"remote": false,
"fetchDetail": false,
"maxItems": 50,
"maxConcurrency": 4,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# 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 = {
"keywords": "software engineer",
"location": "United States",
"datePosted": "any"
};
// Run the Actor and wait for it to finish
const run = await client.actor("xtracto/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 = {
"keywords": "software engineer",
"location": "United States",
"datePosted": "any",
}
# Run the Actor and wait for it to finish
run = client.actor("xtracto/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 '{
"keywords": "software engineer",
"location": "United States",
"datePosted": "any"
}' |
apify call xtracto/linkedin-jobs-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=xtracto/linkedin-jobs-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LinkedIn Jobs Scraper",
"description": "Search public LinkedIn job postings by keyword + location and get structured listings — title, company, location, posted date and job link — with an option to pull the full job description, seniority, employment type, applicant count and salary. Public job postings only; no login.",
"version": "0.1",
"x-build-id": "Cby1R1mWEoqAV4h1G"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/xtracto~linkedin-jobs-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-xtracto-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/xtracto~linkedin-jobs-scraper/runs": {
"post": {
"operationId": "runs-sync-xtracto-linkedin-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/xtracto~linkedin-jobs-scraper/run-sync": {
"post": {
"operationId": "run-sync-xtracto-linkedin-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"properties": {
"keywords": {
"title": "Search keywords",
"type": "string",
"description": "Job title, skill or keyword to search for. Example: software engineer, product manager, registered nurse."
},
"location": {
"title": "Location",
"type": "string",
"description": "City, state, country or region to search in. Example: United States, London, Remote, New York."
},
"companyId": {
"title": "Company ID (optional)",
"type": "string",
"description": "Show only jobs from one company. This is the company's LinkedIn numeric ID (found in the company page URL). Leave empty to search all companies."
},
"datePosted": {
"title": "Date posted",
"enum": [
"any",
"past24h",
"pastWeek",
"pastMonth"
],
"type": "string",
"description": "Only return jobs posted within this window.",
"default": "any"
},
"remote": {
"title": "Remote jobs only",
"type": "boolean",
"description": "If on, only return jobs marked as remote.",
"default": false
},
"fetchDetail": {
"title": "Fetch full job details",
"type": "boolean",
"description": "If on, the actor opens each job to add the full description, seniority level, employment type, job function, industries, applicant count and salary (when shown). Slower but much richer.",
"default": false
},
"maxItems": {
"title": "Max jobs (0 = all available)",
"minimum": 0,
"type": "integer",
"description": "Overall cap on jobs returned for this run. 0 = all available (LinkedIn shows up to about 1000 results per search).",
"default": 50
},
"maxConcurrency": {
"title": "Max concurrent detail fetches",
"minimum": 1,
"maximum": 12,
"type": "integer",
"description": "How many job pages to open in parallel when \"Fetch full job details\" is on. Keep moderate to stay reliable.",
"default": 4
},
"proxyConfiguration": {
"title": "Proxy",
"type": "object",
"description": "Residential proxy is strongly recommended. If you don't set a country here, the actor defaults to the United States.",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```