# Linkedin Jobs Company Scraper (`scrapecraze/linkedin-jobs-company-scraper`) Actor
🏢 LinkedIn Jobs Company Scraper extracts company insights from LinkedIn—website, industry, size, HQ, followers & more. 🚀 Boost B2B lead gen, sales research, and recruiting with automated, accurate data collection.
- **URL**: https://apify.com/scrapecraze/linkedin-jobs-company-scraper.md
- **Developed by:** [ScrapeCraze](https://apify.com/scrapecraze) (community)
- **Categories:** Lead generation, Jobs, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $1.99 / 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.
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 & Company Scraper ⚡
Searching for relevant LinkedIn job postings and the companies behind them one by one is slow and hard to scale. **LinkedIn Jobs & Company Scraper** helps you automatically pull job details in bulk from a LinkedIn jobs search URL. It’s a strong fit for a LinkedIn job scraper, a LinkedIn company scraper, or anyone scraping LinkedIn jobs and companies for lead generation. Teams doing outreach, competitive research, and recruiting workflows can use it without manual copy-paste. In one run, you can scrape up to your chosen `max_results` and start working with structured job records immediately.
---
### See the Data: Sample Output
Here's a real record from a single run:
```json
{
"title": "Senior Data Analyst",
"location": "Dhaka, Bangladesh",
"company_name": "Acme Analytics",
"posted_time_ago": "3 days ago",
"posted_datetime": "2026-06-04T09:12:30.123456",
"applicants": "123 applicants",
"base_salary": "$80,000 - $100,000 a year",
"additional_compensation": "Bonus eligible",
"seniority_level": "mid_senior",
"employment_type": "full-time",
"job_function": "data_analytics",
"industries": "information_technology",
"description_text": "Join our team to build dashboards and work with stakeholders...\n\nRequirements...",
"description_html": "Join our team...
",
"job_urn": "urn:li:jobPosting:1234567890",
"job_url": "https://www.linkedin.com/jobs/view/1234567890",
"apply_type": "External",
"apply_url": "https://www.linkedin.com/jobs/view/1234567890",
"recruiter_name": "Jane Recruiter",
"recruiter_detail": "Talent Partner",
"recruiter_image": "https://example.com/recruiter.jpg",
"recruiter_profile": "https://www.linkedin.com/in/jane-recruiter/",
"company_profile": "https://www.linkedin.com/company/acme-analytics/",
"company_logo": "https://example.com/company-logo.png",
"company_id": "9876543210"
}
````
**Output Fields**
| Field | Type | What It Tells You |
|---|---|---|
| `title` | string | The job title you can match to your target roles. |
| `location` | string | Where the role is based to filter by region. |
| `company_name` | string | The company offering the job so you can build company lists. |
| `posted_time_ago` | string | How recently the job appears (useful for freshness checks). |
| `posted_datetime` | string | A computed datetime you can sort and track over time. |
| `applicants` | string | Applicant volume text for rough market signal and prioritization. |
| `base_salary` | string | Any salary information surfaced in the listing (when available). |
| `additional_compensation` | string | Extra compensation or incentives when present. |
| `seniority_level` | string | Seniority extracted from job criteria for better filtering. |
| `employment_type` | string | Employment type (for example, full-time/part-time) if provided. |
| `job_function` | string | The job function category from the listing criteria. |
| `industries` | string | Industry tag extracted from job criteria for segmentation. |
| `description_text` | string | Plain-text job description for analysis and keyword matching. |
| `description_html` | string | The HTML version of the description if you need richer formatting. |
| `job_urn` | string | A stable identifier for the job posting in the dataset. |
| `job_url` | string | Direct link back to the job posting for validation. |
| `apply_type` | string | Whether the job uses “External” or “Easy Apply” flow. |
| `apply_url` | string | The apply link captured alongside job details. |
| `recruiter_name` | string | Recruiter or job poster name when available on the page. |
| `recruiter_profile` | string | Link to the recruiter profile area when captured. |
| `company_profile` | string | Link to the company page when available. |
| `company_logo` | string | Company logo URL/asset when captured. |
| `company_id` | string | Company identifier extracted from the job page details. |
| `error_message` | string | Not included in the pushed dataset objects; if a job fetch fails, the record is skipped and you simply get fewer results. |
Export your full dataset as JSON, CSV, or Excel from the Apify dashboard.
***
### Setting It Up
Drop this into your `input.json` and you're ready to go:
```json
{
"search_url": "https://www.linkedin.com/jobs/jobs-in-bangladesh",
"max_results": 50
}
```
| Parameter | Required | What It Does |
|---|---:|---|
| `search_url` | âś… | The LinkedIn jobs search URL that the actor uses as the starting point for scraping LinkedIn job postings. |
| `max_results` | âś… | Sets the maximum number of jobs to scrape in this run (must be at least 1). |
***
### What It Does
LinkedIn Jobs & Company Scraper scrapes job postings and fetches detailed job information from the job detail pages, then writes structured job records to your Apify dataset as each job is processed.
#### Scrape LinkedIn jobs and company details in one pass
The actor collects job-level fields like `title`, `location`, `company_name`, description content, and application info, so you can build both job and company datasets from the same run. This makes it useful for LinkedIn jobs data scraping and “scrape LinkedIn jobs and companies” workflows.
#### Extract structured job criteria for filtering
It returns job criteria such as `seniority_level`, `employment_type`, `job_function`, and `industries` when those criteria are present on the listing. That means your downstream analysis doesn’t start from unstructured text alone.
#### Works best as a LinkedIn job scraper for bulk research
By using your provided `search_url` and `max_results`, you can generate a larger list quickly without manually opening each posting. This is especially helpful for LinkedIn job vacancies scraper use cases and LinkedIn lead generation scraper pipelines.
#### Clean output ready for analysis
Job details include both `description_text` and `description_html`, plus direct `job_url` and `apply_url`. This gives analysts the flexibility to run text analytics or keep links for review.
#### Includes resilience when pages fail to load
The actor uses retry logic and will stop pagination when it can’t fetch further results. If individual job details can’t be fetched, those jobs are skipped rather than crashing the entire run.
Overall, LinkedIn Jobs & Company Scraper turns a single jobs search page into a structured dataset you can use immediately—perfect for LinkedIn jobs and company scraper tooling.
***
### Why LinkedIn Jobs & Company Scraper?
There are plenty of ways to pull data from LinkedIn job pages—here's why LinkedIn Jobs & Company Scraper stands out.
#### Job details returned as consistent JSON objects
Each scraped job is pushed to your dataset with a consistent set of fields like `title`, `company_name`, `job_url`, and recruiter/company fields when available. That makes it easier to plug into reporting, enrichment, or automation without manual cleanup.
#### Straightforward run control with `max_results`
You can cap the run with `max_results`, which helps you run tests, gather enough data for an experiment, and then scale predictably. This is a common requirement for LinkedIn jobs scraping software workflows that need repeatable output volume.
#### Built for reliability with retries and fallbacks
The actor implements retries and handles blocked or failed requests so your run can continue when possible. It also attempts multiple ways to populate recruiter and salary-related fields when they appear in different parts of the page.
***
### Real-World Use Cases
Here's how different teams put LinkedIn Jobs & Company Scraper to work:
**Recruiting Teams**\
A recruiting coordinator needs a fresh list of roles matching specific seniority and employment type preferences. They run the actor on a relevant jobs search URL, then quickly filter the dataset by extracted `seniority_level` and `employment_type` to focus on the most promising postings.
**Sales and BD Teams**\
A sales team building a pipeline wants to know which companies are actively hiring. They use LinkedIn Jobs & Company Scraper to scrape LinkedIn job postings with `company_name`, `company_profile`, and job URLs, then prioritize accounts based on recency (`posted_time_ago`) and role fit (`job_function`).
**Marketing Agencies**\
An agency running competitive messaging research needs to understand what roles companies are advertising. They pull job descriptions (`description_text`) at scale, then extract themes that guide campaign angles and target-vertical positioning.
**Freelance Researchers & Data Analysts**\
A researcher is comparing hiring trends across locations and categories. They scrape a defined number of results (`max_results`) and use `posted_datetime` for time-based analysis while keeping direct `job_url` links for validation.
**Automation Specialists / Developers**\
A developer wants a repeatable job-data ingestion step for an internal dashboard. They trigger LinkedIn job search scraping automation with the Apify Actor, ingest the dataset objects into their system, and schedule periodic runs to keep the dataset up to date.
***
### How to Run It
No code required. Here's how to get your first results in under 5 minutes:
1. **Open the actor on Apify** — go to the Apify actor page at [console.apify.com](https://console.apify.com).
2. **Enter your inputs** — set `search_url` to your LinkedIn jobs search URL and choose `max_results`.
3. **Configure proxy settings (optional)** — if your environment needs it, enable proxy support in the run configuration.
4. **Start the run** — open the live logs to confirm the actor is processing jobs.
5. **Open the Dataset tab** — you’ll see job records pushed into the dataset as they’re fetched.
6. **Export your results** — download JSON, CSV, or Excel from the dataset view.
7. **Refine and re-run** — adjust `search_url` and `max_results` to match your target region or research scope.
The whole setup takes under 5 minutes — results start appearing within seconds of launch.
***
### Export & Integration Options
Once your data is collected, LinkedIn Jobs & Company Scraper fits directly into your existing workflow.
You can download results in your preferred format (JSON, CSV, or Excel) from the Apify dashboard dataset tab. If you want to connect this output to other tools, you can use Apify’s typical automation routes like Zapier/Make-style flows, or access results programmatically via the Apify API.
If you have downstream steps that should run automatically, you can also integrate run completions with webhooks so your pipeline continues as soon as the dataset is ready.
***
### Pricing
LinkedIn Jobs & Company Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. The free tier provides $5 platform credits on sign-up, which is typically enough for several real test runs.
After that, runs are billed per Actor compute unit (CU) and you only pay for platform compute. For heavier workloads, use Apify’s subscription plans, and for exact details check the Apify pricing page.
Start free at [apify.com](https://apify.com) — scale up when you need to.
***
### Reliability & Limitations
| What We Handle | How |
|---|---|
| Rate limits / blocked requests | Retries and resilience handling during fetch operations |
| Failed search pagination | Stops pagination when it can’t fetch further results |
| Missing or partial fields | Fields may be `null` when a specific detail isn’t available on the page |
| Individual job detail failures | Jobs that fail to fetch are skipped rather than failing the whole run |
**Limitations:** This actor works with publicly accessible pages it can load during scraping. If a page doesn’t provide certain details (like salary or recruiter fields), those fields may be missing or empty. Private or login-gated content isn’t accessible.
For enterprise-scale needs or custom configurations, reach out and we’ll help.
***
### Frequently Asked Questions
#### Is there a free plan?
Yes. Apify offers a free tier with credits for testing runs, so you can try LinkedIn Jobs & Company Scraper before scaling up.
#### Do I need to log in or create an account on LinkedIn?
You only need the `search_url` and your run configuration inputs. You do not provide LinkedIn credentials as part of this actor’s input schema.
#### How accurate is the extracted data?
The extracted fields reflect what’s present on the job pages at the time of scraping. When specific details are not shown on a listing, the corresponding fields won’t be available.
#### How many results can I get per run?
You can control this with `max_results`, which sets the maximum number of jobs the actor attempts to scrape in one run.
#### How fresh is the data?
Freshness depends on how recently the jobs search results and job pages show updated “posted” information. The actor captures `posted_time_ago` and a computed `posted_datetime` when it can.
#### Is this legal? Does it comply with GDPR / CCPA?
This actor is designed to scrape **publicly available data** that is visible without logging in. You are responsible for ensuring your use complies with GDPR, CCPA, platform Terms of Service, and any applicable local regulations.
#### Can I export to Google Sheets or Excel?
Yes. You can export from the Apify dashboard dataset tab, including JSON, CSV, or Excel. From there, you can import into Google Sheets or any BI tool that accepts those formats.
#### Can I schedule this to run automatically?
Yes—Apify supports scheduled runs as part of its platform capabilities. You can set the actor to run automatically and refresh your dataset on a cadence.
#### Can I access results via the API?
Yes. Apify provides API access for actor runs and dataset retrieval, so you can pull results programmatically and automate downstream processing.
#### What happens when the actor encounters an error?
If the actor can’t fetch search results, it stops pagination. If fetching job details fails for a specific job, that job is skipped so the run can continue where possible.
***
### Get Help & Use Responsibly
Got a question about LinkedIn Jobs & Company Scraper or a feature you'd like added? Reach out at . We’re happy to help with practical guidance and we actively maintain this actor based on feedback—ideas like additional filtering options or more robust field normalization are welcome.
***
**publicly available data** only. The actor does not access private accounts, login-gated pages, or password-protected content. You’re responsible for complying with GDPR, CCPA, and the platform’s Terms of Service when using the data. For data-removal requests, contact . Use responsibly, ethically, and only for lawful purposes.
# Actor input Schema
## `search_url` (type: `string`):
LinkedIn jobs search URL
## `max_results` (type: `integer`):
Maximum number of jobs to scrape
## Actor input object example
```json
{
"search_url": "https://www.linkedin.com/jobs/jobs-in-bangladesh",
"max_results": 50
}
```
# 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 = {
"search_url": "https://www.linkedin.com/jobs/jobs-in-bangladesh"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapecraze/linkedin-jobs-company-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`đź’ľ 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 = { "search_url": "https://www.linkedin.com/jobs/jobs-in-bangladesh" }
# Run the Actor and wait for it to finish
run = client.actor("scrapecraze/linkedin-jobs-company-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("đź’ľ 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 '{
"search_url": "https://www.linkedin.com/jobs/jobs-in-bangladesh"
}' |
apify call scrapecraze/linkedin-jobs-company-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapecraze/linkedin-jobs-company-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Linkedin Jobs Company Scraper",
"description": "🏢 LinkedIn Jobs Company Scraper extracts company insights from LinkedIn—website, industry, size, HQ, followers & more. 🚀 Boost B2B lead gen, sales research, and recruiting with automated, accurate data collection.",
"version": "0.1",
"x-build-id": "WsBcl8U2Nq13NGw9V"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapecraze~linkedin-jobs-company-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapecraze-linkedin-jobs-company-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapecraze~linkedin-jobs-company-scraper/runs": {
"post": {
"operationId": "runs-sync-scrapecraze-linkedin-jobs-company-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapecraze~linkedin-jobs-company-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrapecraze-linkedin-jobs-company-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"search_url",
"max_results"
],
"properties": {
"search_url": {
"title": "Search URL",
"type": "string",
"description": "LinkedIn jobs search URL"
},
"max_results": {
"title": "Max Results",
"minimum": 1,
"type": "integer",
"description": "Maximum number of jobs to scrape",
"default": 50
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```