# LinkedIn Job Details Scraper – Descriptions & Data (`scrapingmonkey/linkedin-job-details-scraper`) Actor

Extract public LinkedIn job details from URLs or IDs. Get descriptions, employers, hiring criteria, application links, and published salary and benefits fields.

- **URL**: https://apify.com/scrapingmonkey/linkedin-job-details-scraper.md
- **Developed by:** [ScrapingMonkey](https://apify.com/scrapingmonkey) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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.
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?

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

Turn known LinkedIn vacancies into detailed job records. **LinkedIn Job Details Scraper** extracts public descriptions, employer information, hiring criteria, application links, and salary or benefits fields when the listing exposes them.

Submit job URLs or numeric IDs to enrich a shortlist, analyze requirements, or add readable descriptions to a vacancy database. Each unique job produces one row, with its details available in the same table and JSON export.

| At a glance | Details |
|---|---|
| 📥 Input | LinkedIn job URLs or numeric job IDs |
| 📤 Output | Descriptions, employer, location, criteria, application details, and available compensation |
| 🧩 Format | One row per unique processed job ID |
| 🔐 Login required | No LinkedIn credentials or cookies |
| 💾 Delivery | Apify dataset for JSON, spreadsheet exports, and API access |

### What the LinkedIn job details scraper extracts 💼

The Actor reads the public job page and returns:

- Job ID, canonical URL, title, and employer identity
- Plain-text and HTML descriptions
- Location, available address details, and workplace type
- Posting date, relative posting text, and closing date when published
- Applicant text and a parsed count, plus the observed open or closed status
- Application URL, seniority, employment type, job function, and industry
- Qualifications, skills, benefits, and published salary fields
- The original labels and values from the job's criteria section

Optional fields depend on what the public listing supplies. A successful row can therefore have a complete description while salary, skills, address, or application details remain empty.

### How to scrape LinkedIn job details 🚀

1. Add a LinkedIn job URL or numeric job ID for each vacancy.
2. Start the Actor.
3. Review the success and failed rows in the dataset.
4. Export the records or join them to job-search results by `id`.

```json
{
  "inputList": [
    "https://www.linkedin.com/jobs/view/4378357766/"
  ]
}
```

The Actor also accepts LinkedIn jobs URLs containing `currentJobId`. Different accepted URLs that resolve to the same numeric job ID are processed once.

### Job detail fields and output 📦

| Field group | Included data |
|---|---|
| Identity | Input, status, job ID, URL, and title |
| Employer and place | Company identity, logo, location, address, and workplace type |
| Description | Text, HTML, qualifications, skills, and benefits |
| Timing and applications | Posting dates, applicant text and count, job status, and application URL |
| Hiring criteria | Seniority, employment type, function, industry, and paired criteria labels and values |
| Compensation | Currency, single value or range, and pay period when published |

Complete illustrative output item with all 39 fields. The vacancy, employer, dates, and compensation below are fictional examples, not a live result:

```json
{
  "input": "9876543210",
  "status": "success",
  "id": "9876543210",
  "url": "https://www.linkedin.com/jobs/view/9876543210/",
  "title": "Software Engineer",
  "company.id": "12345678",
  "company.name": "Example Analytics",
  "company.url": "https://www.linkedin.com/company/example-analytics/",
  "company.logo": "https://example.com/company-logo.png",
  "location": "Singapore",
  "address.street": "",
  "address.city": "Singapore",
  "address.region": "",
  "address.postal_code": "",
  "address.country": "SG",
  "workplace_type": "Hybrid",
  "description": "Build data services and reliable internal tools. Experience with Python and SQL is required.",
  "description_html": "<p>Build data services and reliable internal tools. Experience with Python and SQL is required.</p>",
  "posted_at": "2026-09-06",
  "posted_time": "2 days ago",
  "valid_through": "2026-10-06",
  "applicants_text": "32 applicants",
  "applicants": 32,
  "job_status": "open",
  "apply_url": "https://example.com/careers/software-engineer",
  "seniority_level": "Mid-Senior level",
  "employment_type": "Full-time",
  "job_function": "Engineering",
  "industries": "Software Development",
  "qualifications": "Experience building production software with Python and SQL.",
  "skills": [
    "Python",
    "SQL"
  ],
  "benefits": [
    "Medical insurance",
    "Learning budget"
  ],
  "salary.currency": "SGD",
  "salary.value": 0,
  "salary.min": 90000,
  "salary.max": 120000,
  "salary.unit": "YEAR",
  "criteria.name": [
    "Seniority level",
    "Employment type",
    "Job function",
    "Industries"
  ],
  "criteria.value": [
    "Mid-Senior level",
    "Full-time",
    "Engineering",
    "Software Development"
  ]
}
```

One job occupies one row in one table. JSON keys such as `company.name` and `salary.min` are literal field names matching the columns. The entries in `criteria.name` and `criteria.value` match by position; skills and benefits remain lists in the same row.

Successful results preserve empty strings, empty lists, and default numeric values when the public page omits information. In particular, `0` in an applicant or salary field can mean no value was supplied, not a verified zero. A failed result retains `input`, sets `status` to `failed`, and uses `null` for all job-data fields.

### Input requirements and job coverage ⚙️

| Parameter | Type | Required | Rules |
|---|---|---|---|
| `inputList` | array of strings | Yes | At least one LinkedIn job URL or numeric job ID |

Accepted forms include `4378357766`, `https://www.linkedin.com/jobs/view/4378357766/`, and `https://www.linkedin.com/jobs/search/?currentJobId=4378357766`. Numeric IDs must contain at least six digits. Full URLs must use HTTP or HTTPS on LinkedIn and identify a job within a jobs path.

There is no pagination setting: each accepted target refers to one vacancy. Collect IDs with Job Search Scraper or Company Jobs Scraper before using this Actor to enrich them.

Closed jobs can still produce a successful detail row if LinkedIn exposes their content. Deleted, restricted, or otherwise unavailable pages can produce failed rows. Neither a valid URL nor a successful response guarantees every optional field.

### LinkedIn job details use cases 🎯

#### Vacancy database enrichment

Add descriptions, application links, and employer details to job IDs collected from search or company listings.

#### Skills and requirements research

Analyze the description alongside published skills, qualifications, and hiring criteria to compare requirements across roles.

#### Compensation research

Collect published salary values with their currency and period. Separate absent values before aggregating ranges or comparing employers.

#### Application workflow preparation

Organize a shortlist with role descriptions, location, public application links, and the observed listing status.

### Pricing and billable job rows 💰

Check the Actor's **Pricing** tab for the current charging model and rate.

Under dataset-item pricing, each saved success or failed row can count as one result. Accepted inputs that resolve to the same job ID are processed once, and automatic request retries do not independently create extra rows.

Descriptions, criteria, skills, and benefits remain inside the job row. They do not create separate dataset items.

### LinkedIn job details API and integrations 🔌

Set `$ACTOR_ID` to the ID shown in the Actor's API tab and `$APIFY_TOKEN` to your Apify token:

```bash
curl -X POST "https://api.apify.com/v2/acts/$ACTOR_ID/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputList":["https://www.linkedin.com/jobs/view/4378357766/"]}'
```

Call the Actor from REST, Apify client libraries, schedules, or webhooks. Send its default dataset to Google Sheets, Make, Zapier, a research database, or your own application. Use job IDs to join detailed records to the listings that selected them.

### Reliability and public listing limits ⚠️

The Actor processes up to five requests concurrently and retries temporary request failures. Individual invalid or unavailable jobs are saved as failed rows so other jobs in a mixed batch can continue.

Applicant text may be approximate, and its parsed count should be read together with the original text. The open or closed label reflects the page's visible closure signal; it is not confirmation that the employer is still accepting applications.

LinkedIn can remove listings, restrict public access, or change the fields it exposes. Image and application URLs can change, and optional compensation or criteria fields may be absent. An inability to save the dataset can stop the run.

### Frequently asked questions ❓

#### Can I submit IDs returned by Job Search Scraper?

Yes. Use its job IDs or URLs in `inputList`. Company Jobs Scraper results can be enriched in the same way.

#### Does it always return a salary?

Salary fields are populated when published in the data available to the Actor. Missing numeric values can be `0`; do not interpret them as a salary offer.

#### Can it retrieve closed jobs?

Yes, if the public job details remain accessible. The row can succeed with `job_status` set to `closed`. Removed or inaccessible listings may fail.

#### Is the applicant count exact?

The Actor preserves applicant text and parses a number from it. If LinkedIn uses a phrase such as “over 100 applicants,” the number retains that limitation.

#### Does it require LinkedIn login?

No LinkedIn account, password, or session cookies are required.

### Support, responsible use, and related actors 🛟

For a reproducible problem, share the run ID, a public job URL or ID, and the field or behavior you expected through the Actor's support channel.

Use public listing content in accordance with applicable copyright, privacy, contractual, and platform requirements.

# Actor input Schema

## `inputList` (type: `array`):

Add a LinkedIn /jobs/view/ URL, a jobs URL containing currentJobId, or a numeric job ID.

## Actor input object example

```json
{
  "inputList": [
    "https://www.linkedin.com/jobs/view/4378357766/"
  ]
}
```

# Actor output Schema

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

Actor dataset rows. Use the status column to distinguish successful extraction from failed processing.

# 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 = {
    "inputList": [
        "https://www.linkedin.com/jobs/view/4378357766/"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapingmonkey/linkedin-job-details-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 = { "inputList": ["https://www.linkedin.com/jobs/view/4378357766/"] }

# Run the Actor and wait for it to finish
run = client.actor("scrapingmonkey/linkedin-job-details-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 '{
  "inputList": [
    "https://www.linkedin.com/jobs/view/4378357766/"
  ]
}' |
apify call scrapingmonkey/linkedin-job-details-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapingmonkey/linkedin-job-details-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/FDuWGhVsqaDkMZ3Ac/builds/gQIn1VgF8zvwjUcNx/openapi.json