# LinkedIn Company Jobs Scraper (`apt_marble/linkedin-company-jobs-scraper`) Actor
Scrape all open jobs from any company's LinkedIn presence: titles, locations, descriptions, salaries, seniority and applicant counts. Includes a hiring-signals summary showing where and what each company is hiring. Export to JSON, CSV or Excel. No login or cookies required.
- **URL**: https://apify.com/apt\_marble/linkedin-company-jobs-scraper.md
- **Developed by:** [Hamza](https://apify.com/apt_marble) (community)
- **Categories:** Jobs, Social media, Lead generation
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
$2.50 / 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/platform/actors/running/actors-in-store#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.
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 Company Jobs Scraper 🏢
Type in a company — get **every open job it has on LinkedIn**, as clean structured JSON, plus a ready-made **hiring-signals summary** telling you who is scaling which function, where.
Where keyword scrapers answer *"what jobs match this search?"*, this Actor answers the question sales, recruiting and market-intelligence teams actually ask: *"what is **this company** hiring for right now?"*
### ✨ What you get
- 🏢 **Company-centric full export** — enter company **names** ("Microsoft"), **numeric LinkedIn ids** ("1035") or **company-page URLs** (`linkedin.com/company/microsoft`); mixed lists are fine. Every input is resolved automatically and the resolution is logged.
- 📄 **Complete job records** — title, location, posted date, salary (when shown), full description, seniority level, employment type, job function, industries and applicant count.
- 📊 **Hiring-signals summary per company** — job volume, top 5 locations, top 10 title words, on-site/remote/hybrid mix, seniority mix and the posting-date window — saved as a `SUMMARY` record next to the dataset and printed in the log.
- 🎛️ **All the LinkedIn filters** — narrow each company's listing by keyword, location, date posted, job type, experience level and workplace type.
- 🔁 **Deduplicated & rank-tagged** — one record per job, no repeats across pages, each carrying a stable `position` field with its listing rank (records may arrive in the dataset out of rank order while details are fetched concurrently — sort by `position` to restore it).
- ⚡ **Fast** — job details are fetched concurrently; a 100-job company typically exports in a couple of minutes.
### 💼 Why it's valuable
Open roles are the most honest public signal a company emits. This Actor turns that signal into data you can act on:
- A surge of **sales roles in a new region** → territory expansion (and a buying window for your product).
- Dozens of **platform/infra engineer postings** → a re-platforming effort → perfect timing for dev-tool vendors.
- **Seniority mix** shifting toward directors → new org being built from the top down.
- Posting-date window shows whether hiring is **accelerating or stale**.
### ⚙️ Options
| Input | What it does |
| --- | --- |
| `companies` | Company names, numeric ids, or `linkedin.com/company/...` URLs (mix freely) |
| `maxJobsPerCompany` | Cap per company (default 100; LinkedIn's public listing serves at most ~1,000) |
| `scrapeJobDetails` | Fetch each job's full description + criteria (default on) |
| `keywords`, `location` | Narrow the export inside each company |
| `datePosted` | `past-24h` / `past-week` / `past-month` |
| `jobType`, `experienceLevel`, `workplaceType` | Same options as LinkedIn's own filter panel |
| `sortBy` | `recent` (default) or `relevance` |
### 🚀 Example
Input:
```json
{
"companies": ["Microsoft", "https://www.linkedin.com/company/stripe", "1441"],
"maxJobsPerCompany": 100,
"scrapeJobDetails": true,
"datePosted": "past-month",
"workplaceType": ["remote", "hybrid"]
}
````
(Three input forms in one list: a name, a company-page URL, and a numeric id — `1441` is Google's.)
One dataset record per job:
```json
{
"jobId": "4440979323",
"title": "Circular Center Program Manager",
"url": "https://www.linkedin.com/jobs/view/4440979323",
"companyInput": "Microsoft",
"companyId": "1035",
"companyResolvedName": "Microsoft",
"companyName": "Microsoft",
"companyUrl": "https://www.linkedin.com/company/microsoft",
"companyLogo": "https://media.licdn.com/dms/image/v2/…/microsoft_logo",
"location": "Cheyenne, WY",
"postedAt": "2026-07-15T00:00:00.000Z",
"postedTimeText": "1 week ago",
"salary": "$98,000.00/yr - $208,800.00/yr",
"seniorityLevel": "Mid-Senior level",
"employmentType": "Full-time",
"jobFunction": "Program and Project Management",
"industries": "Software Development",
"applicantsCount": 25,
"applicantsText": "25 applicants",
"descriptionText": "Microsoft Cloud Operations and Innovation is the team behind the cloud...",
"descriptionHtml": "Microsoft Cloud Operations and Innovation is the team behind...
",
"position": 1,
"companyJobsUrl": "https://www.linkedin.com/jobs/search?f_C=1035",
"scrapedAt": "2026-07-24T12:34:56.789Z"
}
```
And the `SUMMARY` key-value record aggregates each company:
```json
{
"generatedAt": "2026-07-24T12:35:10.123Z",
"companies": [{
"company": "Microsoft",
"companyId": "1035",
"jobsFound": 100,
"topLocations": [{ "location": "Redmond, WA", "count": 22 }],
"topTitleWords": [{ "word": "engineer", "count": 31 }, { "word": "software", "count": 24 }],
"byWorkplaceType": { "unspecified": 100 },
"bySeniority": { "Mid-Senior level": 58, "Director": 9 },
"newestPostedAt": "2026-07-23T00:00:00.000Z",
"oldestPostedAt": "2026-05-02T00:00:00.000Z"
}],
"unresolved": []
}
```
### 💡 Use cases
- **Sales intelligence** — detect scaling teams and open buying windows from hiring surges.
- **Competitor tracking** — schedule weekly runs per competitor and diff the exports.
- **Recruiting & sourcing** — map who else is hiring the profiles you need, and where.
- **Market research** — quantify functions, locations and seniority across a portfolio of companies.
- **Job boards & alerts** — keep a company-scoped feed fresh with `datePosted: "past-24h"`.
### 💳 Pricing
Pay per result — you're charged a small fee per job record pushed to the dataset, nothing else. Set a maximum budget per run in the Actor's settings; the run stops cleanly when it's reached.
### ❓ FAQ
**How do I find a company's numeric id?** You don't have to — names and company-page URLs resolve automatically (the log shows exactly what each input resolved to). Ids just skip the lookup.
**What if a company can't be resolved?** It's logged as a warning, listed under `unresolved` in the `SUMMARY` record, and the run continues with the rest.
**Does it need my LinkedIn account?** No. Only publicly visible job postings are scraped — no login, no cookies.
**Can I get more than 1,000 jobs for a huge company?** LinkedIn's public listing stops serving results around 1,000 per query. Slice bigger companies with filters (e.g. one run per `location` or `jobType`) to pull deeper.
### ⚠️ Honest limitations
- **~1,000-job ceiling per company/filter combination** — a LinkedIn-side limit on the public listing (see FAQ for the slicing workaround).
- **Salary appears only when the posting displays it**; applicant counts are the coarse public values ("Over 200 applicants" → 200).
- **Workplace mix is often mostly `unspecified`** — it is derived from location suffixes like "(Remote)" / "(Hybrid)", which company-listing cards frequently omit. For an exact split, run with a single `workplaceType` filter (one run per type): the bucket is then known from the filter itself.
- **Company name resolution picks the best public match** — for ambiguous brands, pass the numeric id or the exact company-page URL instead.
- Listings are living pages: consecutive runs can differ slightly as jobs are posted, closed or re-ranked.
# Actor input Schema
## `companies` (type: `array`):
The companies to export jobs for. Each entry can be a company name ("Microsoft"), a numeric LinkedIn company id ("1035") or a company page URL ("https://www.linkedin.com/company/microsoft"). Names and URLs are resolved automatically and the resolution is logged.
## `maxJobsPerCompany` (type: `integer`):
How many jobs to scrape for each company. LinkedIn's public listing serves at most ~1,000 jobs per company/filter combination.
## `scrapeJobDetails` (type: `boolean`):
Opens each job to capture the full description, seniority level, employment type, job function, industries and applicant count. Switch off for a faster, list-level export (title, company, location, posted date, salary).
## `keywords` (type: `string`):
Optional keyword filter applied inside each company's listing, e.g. "machine learning" to only export ML roles.
## `location` (type: `string`):
Optional geographic filter, e.g. "United States", "London, England, United Kingdom" or "Remote".
## `datePosted` (type: `string`):
Only include jobs posted within this window.
## `jobType` (type: `array`):
Restrict to one or more employment types.
## `experienceLevel` (type: `array`):
Restrict to one or more experience levels.
## `workplaceType` (type: `array`):
Restrict to one or more workplace arrangements.
## `sortBy` (type: `string`):
How LinkedIn orders each company's listing. "Most recent" is recommended for hiring-signal tracking.
## `proxyCountry` (type: `string`):
Two-letter country code for the request IP. Supported: us, gb (or uk), ca, au, de, fr, es, it, nl, ie, in, sg — any other code falls back to "us" (with a warning in the log). Leave as "us" unless you need the results served from a specific country.
## `maxConcurrency` (type: `integer`):
How many job-detail pages to fetch in parallel while enriching a company's jobs.
## Actor input object example
```json
{
"companies": [
"Microsoft"
],
"maxJobsPerCompany": 25,
"scrapeJobDetails": true,
"keywords": "",
"location": "",
"datePosted": "",
"jobType": [],
"experienceLevel": [],
"workplaceType": [],
"sortBy": "recent",
"proxyCountry": "us",
"maxConcurrency": 3
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
## `summary` (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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"companies": [
"Microsoft"
],
"maxJobsPerCompany": 25
};
// Run the Actor and wait for it to finish
const run = await client.actor("apt_marble/linkedin-company-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 = {
"companies": ["Microsoft"],
"maxJobsPerCompany": 25,
}
# Run the Actor and wait for it to finish
run = client.actor("apt_marble/linkedin-company-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 '{
"companies": [
"Microsoft"
],
"maxJobsPerCompany": 25
}' |
apify call apt_marble/linkedin-company-jobs-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=apt_marble/linkedin-company-jobs-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LinkedIn Company Jobs Scraper",
"description": "Scrape all open jobs from any company's LinkedIn presence: titles, locations, descriptions, salaries, seniority and applicant counts. Includes a hiring-signals summary showing where and what each company is hiring. Export to JSON, CSV or Excel. No login or cookies required.",
"version": "0.0",
"x-build-id": "f2qlvxh1iOYlv314u"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/apt_marble~linkedin-company-jobs-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-apt_marble-linkedin-company-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/apt_marble~linkedin-company-jobs-scraper/runs": {
"post": {
"operationId": "runs-sync-apt_marble-linkedin-company-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/apt_marble~linkedin-company-jobs-scraper/run-sync": {
"post": {
"operationId": "run-sync-apt_marble-linkedin-company-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"required": [
"companies"
],
"properties": {
"companies": {
"title": "Companies",
"type": "array",
"description": "The companies to export jobs for. Each entry can be a company name (\"Microsoft\"), a numeric LinkedIn company id (\"1035\") or a company page URL (\"https://www.linkedin.com/company/microsoft\"). Names and URLs are resolved automatically and the resolution is logged.",
"default": [
"Microsoft"
],
"items": {
"type": "string"
}
},
"maxJobsPerCompany": {
"title": "Maximum jobs per company",
"minimum": 0,
"type": "integer",
"description": "How many jobs to scrape for each company. LinkedIn's public listing serves at most ~1,000 jobs per company/filter combination.",
"default": 100
},
"scrapeJobDetails": {
"title": "Scrape full job details",
"type": "boolean",
"description": "Opens each job to capture the full description, seniority level, employment type, job function, industries and applicant count. Switch off for a faster, list-level export (title, company, location, posted date, salary).",
"default": true
},
"keywords": {
"title": "Keywords (narrow within the company)",
"type": "string",
"description": "Optional keyword filter applied inside each company's listing, e.g. \"machine learning\" to only export ML roles.",
"default": ""
},
"location": {
"title": "Location",
"type": "string",
"description": "Optional geographic filter, e.g. \"United States\", \"London, England, United Kingdom\" or \"Remote\".",
"default": ""
},
"datePosted": {
"title": "Date posted",
"enum": [
"",
"past-24h",
"past-week",
"past-month"
],
"type": "string",
"description": "Only include jobs posted within this window.",
"default": ""
},
"jobType": {
"title": "Job type",
"type": "array",
"description": "Restrict to one or more employment types.",
"items": {
"type": "string",
"enum": [
"full-time",
"part-time",
"contract",
"temporary",
"internship",
"volunteer",
"other"
],
"enumTitles": [
"Full-time",
"Part-time",
"Contract",
"Temporary",
"Internship",
"Volunteer",
"Other"
]
},
"default": []
},
"experienceLevel": {
"title": "Experience level",
"type": "array",
"description": "Restrict to one or more experience levels.",
"items": {
"type": "string",
"enum": [
"internship",
"entry-level",
"associate",
"mid-senior",
"director",
"executive"
],
"enumTitles": [
"Internship",
"Entry level",
"Associate",
"Mid-Senior level",
"Director",
"Executive"
]
},
"default": []
},
"workplaceType": {
"title": "On-site / Remote / Hybrid",
"type": "array",
"description": "Restrict to one or more workplace arrangements.",
"items": {
"type": "string",
"enum": [
"on-site",
"remote",
"hybrid"
],
"enumTitles": [
"On-site",
"Remote",
"Hybrid"
]
},
"default": []
},
"sortBy": {
"title": "Sort by",
"enum": [
"relevance",
"recent"
],
"type": "string",
"description": "How LinkedIn orders each company's listing. \"Most recent\" is recommended for hiring-signal tracking.",
"default": "recent"
},
"proxyCountry": {
"title": "IP country (advanced)",
"type": "string",
"description": "Two-letter country code for the request IP. Supported: us, gb (or uk), ca, au, de, fr, es, it, nl, ie, in, sg — any other code falls back to \"us\" (with a warning in the log). Leave as \"us\" unless you need the results served from a specific country.",
"default": "us"
},
"maxConcurrency": {
"title": "Concurrency (advanced)",
"minimum": 1,
"maximum": 10,
"type": "integer",
"description": "How many job-detail pages to fetch in parallel while enriching a company's jobs.",
"default": 3
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```