# LinkedIn Jobs Scraper - No Login, With Salary & Description (`clearfetch/linkedin-jobs-scraper`) Actor

Scrape LinkedIn job search results without login or cookies: title, company, location, post date, applicants, seniority, employment type, industries, salary and full description. Filters for date, remote, experience and job type; new-jobs-only mode for schedules.

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

## Pricing

from $0.70 / 1,000 jobs

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/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, With Salary & Description

Scrape LinkedIn job search results without logging in, cookies or an account: every job's title, company, location,
post date, applicant count, seniority, employment type, job function, industries, salary and full description.
Search by keywords and location with LinkedIn's own filters, or paste LinkedIn search links. **$1.00 per 1,000
jobs.** No proxy needed.

### What data you get

One row per job, same columns every time:

- `title`, `companyName`, `companyUrl`, `companyLogoUrl`, `location`, `url`, `jobId` (stable, use it as a key)
- `postedAt` (exact to the day), `postedText`, `applicants` and `applicantsText`
- `seniorityLevel`, `employmentType`, `jobFunction`, `industries`
- Salary: `salaryMin`, `salaryMax`, `salaryCurrency`, `salaryPeriod`, `salaryText`, `salarySource` (LinkedIn's own pay
  range when it shows one, otherwise the range stated in the description)
- `description` (full text; `descriptionHtml` on request)
- Where it came from: `searchKeywords`, `searchLocation`, `searchUrl`, `rank`

### How to use

1. Type job titles or keywords (one per line) and a location, or paste job search links copied from LinkedIn.
2. Optionally filter by date posted, remote/hybrid/on-site, experience level and job type, and pick how many jobs
   per search.
3. Run it, then download JSON, CSV or Excel, or pull the rows through the API. For a daily feed, schedule it with
   **Only jobs not seen in earlier runs** on.

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `keywords` | array | — | One search per line, e.g. `data engineer`. |
| `location` | string | — | Country, region or city, e.g. `United States`, `Berlin`. `locations` (array) searches several. |
| `startUrls` | array | `[]` | LinkedIn job search links (their filters are kept) or job links. |
| `maxJobsPerSearch` | integer | `100` | Per keyword and location, or per link. 0 = as many as LinkedIn lists (up to 1,000). |
| `datePosted` | string | `any` | `hour`, `day`, `week`, `month`. |
| `workplaceType` | array | `[]` | `onsite`, `remote`, `hybrid`. |
| `experienceLevel` | array | `[]` | `internship`, `entry`, `associate`, `mid-senior`, `director`, `executive`. |
| `jobType` | array | `[]` | `full-time`, `part-time`, `contract`, `temporary`, `internship`, `volunteer`, `other`. |
| `sortBy` | string | `relevance` | `relevance` or `recent`. |
| `fetchDetails` | boolean | `true` | Read each job's page for the description, applicants, seniority, type, function, industries and salary. |
| `includeDescriptionHtml` | boolean | `false` | Keep the description's HTML too. |
| `onlyNew` | boolean | `false` | Leave out (and don't charge) jobs an earlier run already returned. |
| `stateStoreName` | string | `linkedin-jobs-scraper-state` | Where seen jobs are remembered; one name per schedule. |
| `timeoutSecs` | integer | `30` | Per request. |
| `proxyConfiguration` | object | off | Not needed; makes job pages faster on very large runs. |

### Output example

A real row from a run with `"keywords": ["data engineer"], "location": "United States"` on 2026-09-30 (description
shortened here):

```json
{
  "ok": true,
  "type": "job",
  "jobId": "4471769611",
  "url": "https://www.linkedin.com/jobs/view/4471769611/",
  "title": "Enterprise Data- Data Engineer I",
  "companyName": "GBU Life",
  "companyUrl": "https://www.linkedin.com/company/gbu-life",
  "companyLogoUrl": "https://media.licdn.com/dms/image/v2/C4D0BAQFx6MKBZTKkpA/company-logo_100_100/company-logo_100_100/0/1630544932940/gbu_financial_life_logo?e=2147483647&v=beta&t=yqCgevXY2KMtTTe7JwzyXkY0w_8TisysaSklV5gau3I",
  "location": "Pittsburgh, PA",
  "workplaceType": null,
  "postedAt": "2026-09-29T00:00:00.000Z",
  "postedAtApproximate": false,
  "postedText": "12 hours ago",
  "applicants": 106,
  "applicantsText": "106 applicants",
  "seniorityLevel": "Not Applicable",
  "employmentType": "Volunteer",
  "jobFunction": "Information Technology",
  "industries": "Insurance",
  "salaryMin": 82000,
  "salaryMax": 92000,
  "salaryCurrency": "USD",
  "salaryPeriod": "year",
  "salaryText": "$82,000.00/yr - $92,000.00/yr",
  "salarySource": "linkedin",
  "benefits": null,
  "description": "Job Summary\n\nThe Data Engineer is responsible for designing, developing, implementing, and maintaining scalable and reliable data pipelines and infrastructure that support GBU Life’s enterprise data initiatives. This position plays an important role in …",
  "searchKeywords": "data engineer",
  "searchLocation": "United States",
  "searchUrl": "https://www.linkedin.com/jobs/search/?keywords=data%20engineer&location=United%20States",
  "rank": 12,
  "detailed": true,
  "isNew": null,
  "scrapedAt": "2026-09-30T09:13:30.925Z"
}
```

A search or link that cannot be read comes back as one row with `ok: false` and a plain reason, such as
`job not found or no longer open` or `not a LinkedIn link`. Those rows are free.

### Pricing

**$1.00 per 1,000 jobs** ($0.001 each). A job found by two of your searches is written and charged once. Failed
inputs, jobs left out by **Only jobs not seen in earlier runs**, and jobs whose page could not be read (they still
come with their search data and a `detailError`) are free.

Paid Apify plans pay less: 10% off on Bronze, 20% on Silver and 30% on Gold and higher tiers.

### Use cases

- **Job boards and aggregators**: fresh LinkedIn listings for any role and region, with full descriptions.
- **Recruiting and sales intelligence**: which companies are hiring for what, where, and how many applicants each
  role draws.
- **Salary research**: pay ranges by title and location, from LinkedIn's pay ranges and pay-transparency text.
- **Job alerts**: a daily schedule with **Only jobs not seen in earlier runs** sends only the new postings to
  Slack, email or a sheet.

### FAQ

**Do I need a LinkedIn account or cookies?** No. The Actor reads the job pages LinkedIn shows to logged-out
visitors. Your account is never involved, so it can't be restricted.

**How fast is it?** Search results come in pages of 10 in well under a second each. Each job's own page is read at
a pace LinkedIn accepts from one IP, about a job a second; turn **Read each job's page** off for search data only,
which is much faster.

**Why are some salaries empty?** Many postings state no pay. When LinkedIn shows a pay range or the description
states one ("between $100,000 and $130,000"), it is filled and `salarySource` says which.

**Does it scrape people?** No. It returns job postings and company names only, no profiles or recruiter details.

**Is it legal?** It reads public job postings anyone can see without logging in. You are responsible for how you
use the data.

### Integrations

Run it from the Apify API or a client library, schedule it in Apify Console, or connect it to n8n, Make, Zapier or
any MCP client through Apify's integrations. Results are available as JSON, CSV, Excel and through the dataset API.

### More tools from clearfetch

- [ATS Jobs Scraper](https://apify.com/clearfetch/ats-jobs-scraper): every open job straight from company career sites on Greenhouse, Lever, Ashby, Workday and more
- [Website Contact Extractor](https://apify.com/clearfetch/website-contact-extractor): emails, phones and social links from company websites
- [Google News Scraper](https://apify.com/clearfetch/google-news-scraper): news about any company, with publisher links

### Changelog

- **1.0.0** (2026-09) — first release: keyword and location search with date, workplace, experience and job type
  filters, search links, job links, full details with salary, new-jobs-only mode.

# Actor input Schema

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

One search per line, as you would type it on LinkedIn Jobs, e.g. "data engineer" or "nurse".

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

A country, region or city as LinkedIn understands it, e.g. "United States", "Berlin", "Remote" or "Worldwide". Several places: use "locations" through the API.

## `startUrls` (type: `array`):

Job search links copied from LinkedIn (every filter in the link is kept) or single job links (linkedin.com/jobs/view/...).

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

Jobs per keyword and location, or per search link. 0 means as many as LinkedIn lists (up to 1,000).

## `datePosted` (type: `string`):

LinkedIn's own date filter.

## `workplaceType` (type: `array`):

On-site, remote, hybrid, or any mix. Empty means all.

## `experienceLevel` (type: `array`):

Empty means all.

## `jobType` (type: `array`):

Empty means all.

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

Most relevant or most recent first.

## `fetchDetails` (type: `boolean`):

Full description, applicants, seniority, employment type, job function, industries and salary from the text. Off is much faster and keeps title, company, location, date and LinkedIn's pay range when shown.

## `includeDescriptionHtml` (type: `boolean`):

Also keep the description's original HTML in descriptionHtml.

## `onlyNew` (type: `boolean`):

For schedules: jobs written by an earlier run with this option are left out and not charged. Remembered for 90 days in a named key-value store.

## `stateStoreName` (type: `string`):

The key-value store that remembers seen jobs for "Only jobs not seen in earlier runs". Use a different name per schedule to track them separately.

## `timeoutSecs` (type: `integer`):

Per request. LinkedIn rate limits are handled by pacing and retries.

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

Not needed. With a proxy, job pages are read faster because the pace per IP no longer applies.

## Actor input object example

```json
{
  "keywords": [
    "data engineer"
  ],
  "location": "United States",
  "startUrls": [],
  "maxJobsPerSearch": 25,
  "datePosted": "any",
  "workplaceType": [],
  "experienceLevel": [],
  "jobType": [],
  "sortBy": "relevance",
  "fetchDetails": true,
  "includeDescriptionHtml": false,
  "onlyNew": false,
  "stateStoreName": "linkedin-jobs-scraper-state",
  "timeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `results` (type: `string`):

Job rows with title, company, location, post date, applicants, seniority, employment type, industries, salary and description. Searches and links that could not be read appear with ok=false and a reason, and are not charged.

# 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": 25
};

// Run the Actor and wait for it to finish
const run = await client.actor("clearfetch/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": 25,
}

# Run the Actor and wait for it to finish
run = client.actor("clearfetch/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": 25
}' |
apify call clearfetch/linkedin-jobs-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,clearfetch/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/tziwXlNxf5bJSx6P4/builds/ywRzTcluPSii95NRQ/openapi.json