# LinkedIn Jobs Scraper: No Login, Full Details & Salary (`torix33/linkedin-jobs-scraper`) Actor

Scrape LinkedIn job postings without an account: title, company, location, salary, applicants, seniority, employment type, job function, industries and full description. Keywords or search URLs, all LinkedIn filters. Fast HTTP, pay per job.

- **URL**: https://apify.com/torix33/linkedin-jobs-scraper.md
- **Developed by:** [Torix](https://apify.com/torix33) (community)
- **Categories:** Jobs, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$3.00 / 1,000 job scrapeds

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 Jobs Scraper — No Login, Full Details & Salary

Scrape **LinkedIn job postings** without a LinkedIn account or cookies. Search by keywords and location (or paste search URLs from your browser) and get, for every job:

**title, company, company page & logo, location, salary (when shown), date posted, number of applicants, seniority level, employment type, job function, industries, apply type and the full job description** (plain text with line breaks + original HTML).

### How to run it in 3 steps

1. **Add your input.** In **Keywords** paste job titles or skills, plus a **Location** — e.g. `data engineer` + `United States`. The form is already filled with a working example, so you can skip this step for a first try.
2. **Press Start.** The prefilled run returns 20 jobs in about 5 s and costs about **$0.060**. Set **Max results (cost cap)** to cap the cost of bigger runs; you only pay for jobs actually delivered.
3. **Get the data.** Open the **Output** tab and export JSON, CSV or Excel — or run it from the API, Make, Zapier, n8n or an AI agent (see below).

### Features

- **No login, no cookies, no risk to your account** — uses LinkedIn's public job pages.
- **All LinkedIn filters:** date posted, job type, experience level, on-site/remote/hybrid, company IDs, Easy Apply, fewer than 10 applicants, sort by date.
- **Paste any LinkedIn search URL** — every filter in it is kept.
- **HTTP only, no browser:** plain HTTP (no browser) through rotating datacenter proxies, deduplicated across searches.
- **Pay per job:** $3 per 1,000 jobs. Turn off *full details* for a quicker card-only scrape.

### Use cases

- **Recruiting & staffing agencies:** find companies hiring for the roles you place — instant lead lists.
- **B2B sales signals:** a company hiring a "Head of Data" probably needs data tools.
- **Job market research:** demand, salaries and seniority mix by skill and city.
- **Job boards & newsletters:** fresh postings every day (`postedWithin: "24h"`, `sortBy: "date"`).

### Input

```json
{
  "keywords": ["data engineer", "react developer"],
  "location": "United States",
  "postedWithin": "week",
  "workplaceTypes": ["remote"],
  "maxJobsPerSearch": 200,
  "scrapeDetails": true
}
```

### Output

#### Real output (default input)

First 2 of 20 records from a run with the prefilled input (4.6 s on a test machine; long values shortened here):

```json
[
  {
    "id": "4454862094",
    "url": "https://www.linkedin.com/jobs/view/4454862094/",
    "title": "Senior Data Engineer, DX",
    "company": "Atlassian",
    "companyUrl": "https://au.linkedin.com/company/atlassian",
    "companyLogo": "https://media.licdn.com/dms/image/v2/D4E0BAQGeyLL6crkJKg/company-logo_100_100/company-logo_100_100/0/1735593604612/atlassian_logo?e=21474836…",
    "location": "Salt Lake City, UT",
    "salary": null,
    "benefits": "Actively Hiring",
    "postedAt": "2026-09-26",
    "postedAgo": "2 weeks ago",
    "slug": "senior-data-engineer-dx-at-atlassian",
    "search": "data engineer",
    "applicants": "Over 200 applicants",
    "applicantsCount": 200,
    "seniorityLevel": "Mid-Senior level",
    "employmentType": "Full-time",
    "jobFunction": null,
    "industries": "Software Development",
    "applyType": "external",
    "applyUrl": null,
    "descriptionText": "Overview\n\nWorking at Atlassian\n\nAtlassians can choose where they work – whether in an office, from home, or a combination of the two. That w…",
    "descriptionHtml": "<strong>Overview<br><br></strong><strong>Working at Atlassian<br><br></strong>Atlassians can choose where they work – whether in an office, …",
    "scrapedAt": "2026-10-15T12:13:54.683Z"
  },
  {
    "id": "4475178472",
    "url": "https://www.linkedin.com/jobs/view/4475178472/",
    "title": "Data Engineer III",
    "company": "Deloitte",
    "companyUrl": "https://www.linkedin.com/company/deloitte",
    "companyLogo": "https://media.licdn.com/dms/image/v2/C560BAQGNtpblgQpJoQ/company-logo_100_100/company-logo_100_100/0/1662120928214/deloitte_logo?e=214748364…",
    "location": "Greater Indianapolis",
    "salary": null,
    "benefits": "Be an early applicant",
    "postedAt": "2026-10-07",
    "postedAgo": "1 week ago",
    "slug": "data-engineer-iii-at-deloitte",
    "search": "data engineer",
    "applicants": "Be among the first 25 applicants",
    "applicantsCount": 25,
    "seniorityLevel": "Not Applicable",
    "employmentType": "Full-time",
    "jobFunction": null,
    "industries": "Accounting, IT Services and IT Consulting, and Business Consulting and Services",
    "applyType": "external",
    "applyUrl": null,
    "descriptionText": "Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make a significant impact on our clie…",
    "descriptionHtml": "Our Deloitte AI &amp; Engineering team works to transform technology platforms, drive innovation, and help make a significant impact on our …",
    "scrapedAt": "2026-10-15T12:13:54.683Z"
  }
]
```

```json
{
  "id": "4476804381",
  "url": "https://www.linkedin.com/jobs/view/4476804381/",
  "title": "Senior React Developer",
  "company": "Programmers.io",
  "companyUrl": "https://www.linkedin.com/company/programmers-io",
  "location": "Rutherford, NJ",
  "salary": null,
  "postedAt": "2026-10-09",
  "postedAgo": "4 days ago",
  "applicantsCount": 55,
  "seniorityLevel": "Mid-Senior level",
  "employmentType": "Contract",
  "jobFunction": "Information Technology",
  "industries": "Software Development",
  "applyType": "easyApply",
  "descriptionText": "Job Title: Senior React Developer\nLocation: …",
  "search": "react developer"
}
```

### Pricing

- **$3.00 per 1,000 jobs** ($0.003 per job).
- The prefilled input returns 20 jobs → **$0.06**.
- Example: 1,000 jobs → $3.00; 10,000 → $30.00. Set **Max results (cost cap)** to limit any run.

* Duplicates across searches are free.
* Your Apify spending limit is respected exactly: the actor stops charging and finishes cleanly when it is reached. Apify platform usage (compute, proxy) is included in the price.

### Use with AI agents (MCP), LangChain, RAG & the API

This actor is a ready-made **tool for AI agents**: an LLM can call it, wait for the run and read clean JSON back.

**MCP server (Claude Desktop, Claude Code, Cursor, VS Code, any MCP client)** — add Apify's hosted MCP server with this actor enabled as a tool:

```json
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=torix33/linkedin-jobs-scraper",
      "headers": { "Authorization": "Bearer <APIFY_TOKEN>" }
    }
  }
}
```

**LangChain / LangGraph** (`pip install langchain-apify`, set `APIFY_API_TOKEN`):

```python
from langchain_apify import ApifyActorsTool, ApifyWrapper
from langchain_core.documents import Document

## 1) give the actor to an agent as a tool
tool = ApifyActorsTool("torix33/linkedin-jobs-scraper")

## 2) or load results as Documents for a RAG index
loader = ApifyWrapper().call_actor(
    actor_id="torix33/linkedin-jobs-scraper",
    run_input={"keywords": ["data engineer", "react developer"], "location": "United States", "postedWithin": "week", "workplaceTypes": ["remote"], "maxJobsPerSearch": 200, "scrapeDetails": True},
    dataset_mapping_function=lambda item: Document(page_content=item["descriptionText"], metadata={"url": item.get("url")}),
)
docs = loader.load()
```

**REST API — one synchronous call, dataset items in the response:**

```bash
curl -X POST "https://api.apify.com/v2/acts/torix33~linkedin-jobs-scraper/run-sync-get-dataset-items?token=<APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["data engineer", "react developer"], "location": "United States", "postedWithin": "week", "workplaceTypes": ["remote"], "maxJobsPerSearch": 200, "scrapeDetails": true}'
```

Also works with the Apify Python/JS clients, **Make, Zapier, n8n**, Google Sheets, webhooks and schedules. Export as JSON, CSV, Excel, XML or RSS.

**RAG tip:** Embed the job `descriptionText` and keep `company`, `location` and `postedAt` as metadata — an agent can then answer "who is hiring for X near me this week?".

### Limitations

- LinkedIn shows at most **1,000 jobs per search**. For more, split by location, date posted or job type.
- Salary is only present when the employer publishes it.
- For very rare keywords or a location LinkedIn does not recognise, LinkedIn itself may return loosely related jobs instead of none — check `title` and `location` in the output and refine the query.
- Keep the Apify datacenter proxy on: LinkedIn rate-limits single IPs very quickly.

### FAQ

**Do I need a LinkedIn account or cookies?**
No. The actor uses LinkedIn's public job pages, so your account is never involved.

**How many jobs per search?**
LinkedIn shows at most 1,000 jobs per search. Split by location, date posted or job type for more.

**Can I paste a LinkedIn jobs search URL?**
Yes. All filters in the URL are kept.

# Actor input Schema

## `keywords` (type: `array`):

Job titles or skills to search, one search per line.

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

City, region or country, e.g. <code>Berlin</code>, <code>United States</code>, <code>Remote</code>.

## `searchUrls` (type: `array`):

Optional: paste job search URLs from your browser (linkedin.com/jobs/search/?…) — all filters in the URL are kept.

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

Only jobs posted within this period.

## `jobTypes` (type: `array`):

Leave empty for all.

## `experienceLevels` (type: `array`):

Leave empty for all.

## `workplaceTypes` (type: `array`):

Leave empty for all.

## `companyIds` (type: `array`):

LinkedIn numeric company IDs (the <code>f_C</code> parameter) to list jobs of specific employers.

## `easyApply` (type: `boolean`):

Only jobs with LinkedIn Easy Apply.

## `under10Applicants` (type: `boolean`):

Only jobs with fewer than 10 applicants so far.

## `sortBy` (type: `string`):

Result order.

## `geoId` (type: `string`):

Optional exact LinkedIn location ID (overrides ambiguous location names).

## `maxJobsPerSearch` (type: `integer`):

LinkedIn shows at most 1,000 jobs per search — split big searches by location or date.

## `scrapeDetails` (type: `boolean`):

Description, applicants, seniority, employment type, job function, industries. Off = search cards only (faster).

## `maxResults` (type: `integer`):

Stop after this many jobs in total. <code>0</code> = no limit.

## `maxConcurrency` (type: `integer`):

Job detail pages fetched in parallel.

## `proxyConfiguration` (type: `object`):

Apify datacenter proxy is enough and keeps costs low.

## Actor input object example

```json
{
  "keywords": [
    "data engineer"
  ],
  "location": "United States",
  "postedWithin": "any",
  "easyApply": false,
  "under10Applicants": false,
  "sortBy": "relevance",
  "maxJobsPerSearch": 20,
  "scrapeDetails": true,
  "maxResults": 0,
  "maxConcurrency": 30,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

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

No description

## `runStats` (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 = {
    "keywords": [
        "data engineer"
    ],
    "location": "United States",
    "maxJobsPerSearch": 20,
    "proxyConfiguration": {
        "useApifyProxy": true
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("torix33/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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "keywords": ["data engineer"],
    "location": "United States",
    "maxJobsPerSearch": 20,
    "proxyConfiguration": { "useApifyProxy": True },
}

# Run the Actor and wait for it to finish
run = client.actor("torix33/linkedin-jobs-scraper").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": [
    "data engineer"
  ],
  "location": "United States",
  "maxJobsPerSearch": 20,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}' |
apify call torix33/linkedin-jobs-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,torix33/linkedin-jobs-scraper"
        }
    }
}
```

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/s99UaLFtGppPEnhDO/builds/j8xtnazmn9JNNEi91/openapi.json