# LinkedIn New Jobs Monitor (`neuton/linkedin-new-jobs-monitor`) Actor

Monitor public LinkedIn job searches without login. Emit evidence-backed new-posting events only after a free baseline.

- **URL**: https://apify.com/neuton/linkedin-new-jobs-monitor.md
- **Developed by:** [Neuton Scripts](https://apify.com/neuton) (community)
- **Categories:** Jobs, Lead generation, Business
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$20.00 / 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.

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

## LinkedIn New Jobs Monitor

Monitor public LinkedIn job searches without login. Emit evidence-backed new-posting events only after a free baseline. Built for recruiters, job boards, sales teams, market researchers, and labor-market platforms.

### Start in 30 seconds

```json
{
  "queries": [
    "AI engineer"
  ],
  "locations": [
    "United States"
  ],
  "monitorKey": "ai-engineer-us",
  "maxResultsPerSearch": 10,
  "maxEvents": 25
}
```

Run the example, inspect the Dataset, and save recurring inputs as Apify Tasks. Export JSON, CSV, Excel, API output, or webhook payloads.

### Choose this Actor when

Use this Actor when you need a repeatable event, comparison, or direct-evidence workflow. For a one-off unfiltered export use [LinkedIn Jobs Search Scraper](https://apify.com/neuton/linkedin-jobs-search-scraper). For complete records from known IDs or URLs use [LinkedIn Job Details Scraper](https://apify.com/neuton/linkedin-job-details-scraper).

### Run a bounded starter Task

[Open the workflow-specific starter Task](https://apify.com/neuton/linkedin-new-jobs-monitor/examples/sample-li-new-jobs-monitor-ai-engineer-us?utm_source=apify_store\&utm_medium=actor_readme\&utm_campaign=linkedin_activation\&utm_content=primary_task) to inspect a small, editable input before running. Keep the first run bounded, then review the evidence fields and `RUN_SUMMARY` before increasing limits or scheduling it.

### Current LinkedIn search behavior

No LinkedIn account or cookies are required. The Actor uses public guest job-search and job-detail pages. LinkedIn controls search ranking and may interpret query wording, so monitors compare stable job IDs within repeated bounded searches rather than claiming complete market coverage. Analyses and events are emitted only when direct public evidence satisfies the documented rule.

For recurring use, keep the same query, location, and monitor key. Prefer small daily checks over occasional deep searches so baseline and change semantics remain auditable.

### First run checklist

1. Run the bounded example input and inspect the Dataset plus `RUN_SUMMARY`.
2. For monitors, confirm the first run creates a free baseline and emits no paid event.
3. Re-run the same Task once to confirm unchanged checks remain free.
4. Add a schedule or webhook only after the event evidence and stable keys match your workflow.

### What you get

- Direct public LinkedIn job URLs and evidence fields
- Stable job IDs, deterministic event or analysis hashes, and explicit caveats
- Pure-HTTP 256 MB execution with bounded retries and safe limits
- No login, cookies, profiles, employees, emails, or private data

### Pricing and billing

Launch PPE price: **$20.00 per 1,000 direct newly posted job events**, or **$2.00 for 100**. The first run creates a free baseline. Unchanged checks emit zero paid rows, and safety-cap failures do not update state.

The first run is a free baseline. Later rows require an unseen job ID and a disclosed posting date later than the previous completed check date. Newly surfaced older or same-day ambiguous cards are free. Diagnostics, duplicates, incomplete fetches, baselines, unchanged checks, and unsupported inferences are never billed.

### Output schema

Every billable row exposes direct source evidence and the fields documented in the Dataset schema. Error-only or placeholder rows are never written.

The separate `RUN_SUMMARY` record reports scanned jobs, saved rows or events, baseline/unchanged state, and failed requests. It is diagnostic and never billed.

### Automation and AI agents

Use Apify Tasks and schedules, webhooks, ChatGPT, Claude, Neuton Actors MCP, n8n, Make, Zapier, Clay, a CRM, warehouse, RAG pipeline, or agent workflow.

Hosted Apify MCP endpoint: `https://mcp.apify.com/?tools=neuton/linkedin-new-jobs-monitor`

### Explore the LinkedIn data family

- [LinkedIn Jobs Search Scraper](https://apify.com/neuton/linkedin-jobs-search-scraper) for broad public-job discovery
- [LinkedIn Job Details Scraper](https://apify.com/neuton/linkedin-job-details-scraper) for complete records from job IDs or URLs
- [LinkedIn Company Hiring Signals](https://apify.com/neuton/linkedin-company-hiring-signals) for evidence-backed company aggregates
- [LinkedIn Salary Jobs Intelligence](https://apify.com/neuton/linkedin-salary-jobs-intelligence) for compensation-bearing postings

### SEO keywords

LinkedIn jobs monitor, LinkedIn job changes, LinkedIn benefits data, talent demand benchmark, visa sponsorship jobs, cross-border hiring intelligence, recruiting intelligence API, Apify LinkedIn Actor.

### Responsible use

Process public job-posting evidence for legitimate recruiting, workforce, compensation, mobility, compliance, and market research. Do not scrape private profiles, bypass access controls, spam people, infer protected traits, or automate unlawful employment decisions. Review LinkedIn's current terms and applicable privacy and employment laws before production use.

# Actor input Schema

## `queries` (type: `array`):

Public LinkedIn job queries or talent segments to inspect. Up to 5 per run.

## `locations` (type: `array`):

Public LinkedIn search locations evaluated independently. Up to 2 per run.

## `maxResultsPerSearch` (type: `integer`):

Maximum public cards inspected for each query and location pair.

## `monitorKey` (type: `string`):

Stable buyer-controlled key that isolates this recurring snapshot.

## `maxEvents` (type: `integer`):

Fail without updating state when a run exceeds this lossless safety cap.

## Actor input object example

```json
{
  "queries": [
    "AI engineer"
  ],
  "locations": [
    "United States"
  ],
  "maxResultsPerSearch": 10,
  "monitorKey": "ai-engineer-us",
  "maxEvents": 25
}
```

# Actor output Schema

## `results` (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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("neuton/linkedin-new-jobs-monitor").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("neuton/linkedin-new-jobs-monitor").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 '{}' |
apify call neuton/linkedin-new-jobs-monitor --silent --output-dataset

```

## MCP server setup

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

```

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/OJtsz0Z4ZDX4PndDp/builds/4k2Q7UsTybf2Dv7zn/openapi.json