# LinkedIn Jobs Scraper & Hiring Signal Intelligence (`azzarilabs/linkedin-jobs-hiring-intelligence`) Actor

Scrape public LinkedIn job listings without login or cookies and turn observed postings into explainable hiring intelligence.

- **URL**: https://apify.com/azzarilabs/linkedin-jobs-hiring-intelligence.md
- **Developed by:** [Azzari Labs](https://apify.com/azzarilabs) (community)
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.50 / 1,000 job 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?

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 & Hiring Signal Intelligence

Scrape public LinkedIn job listings without login or cookies and turn job postings into structured hiring intelligence.

This **LinkedIn Jobs Scraper** finds public jobs, enriches each result from its logged-out job page, and calculates an explainable Hiring Signal Score from evidence observed during the current run. It is a focused **LinkedIn Job Scraper** and an API-ready source of **Job Listings Data**, **Recruiting Data**, and **Labor Market Intelligence**—without a LinkedIn account, user cookies, paid data API, browser, or residential proxy.

### What it does

Enter job keywords and a location. The Actor searches LinkedIn's public logged-out jobs experience, deduplicates postings by public job ID or canonical URL, fetches public job details with moderate concurrency, and writes one real observed job per Dataset row. It also aggregates the delivered evidence by company, role, and location.

The result can be consumed through the Apify API as a practical **LinkedIn Jobs API** workflow. It is not an official LinkedIn API.

### Why it is different

Most job scrapers stop at a list of postings. This Actor adds deterministic **Hiring Intelligence**:

- unique jobs observed per company in this run;
- distinct observed role titles and locations;
- deterministic role clusters;
- an explainable 0–100 Hiring Signal Score;
- HIGH, MEDIUM, or LOW observed-activity levels;
- confidence and plain-English evidence for every score;
- a responsive executive `REPORT.html`.

The score never claims that a company is growing, expanding, or urgently hiring. It measures only the public postings observed inside one run.

### Key features

- Public LinkedIn jobs only; no login, cookies, credentials, or authenticated session
- HTTP-only architecture with no Playwright or Chromium
- Public guest search endpoint with the logged-out public search page as fallback
- Public job-detail enrichment with isolated per-job failures
- Date, workplace, employment, salary, applicant, seniority, industry, company, and apply fields when explicitly exposed
- Missing information remains `null`
- Deduplication by job ID, with canonical job URL fallback
- Moderate concurrency, 15-second HTTP timeouts, two limited retries, and short exponential backoff
- Dataset, machine-readable `OUTPUT`, and executive `REPORT.html`
- Pay Per Event billing prepared for valid unique delivered jobs only

### Input

| Field | Type | Default | Description |
|---|---:|---|---|
| `keywords` | string | `Software Engineer` | Job title, skill, or keyword |
| `location` | string | `United States` | City, region, or country |
| `maxResults` | integer | `10` | Unique jobs to deliver, from 1 to 200 |
| `datePosted` | select | `any` | `any`, `past_24_hours`, `past_week`, `past_month` |
| `workplaceType` | select | `any` | `any`, `remote`, `hybrid`, `onsite` |
| `employmentType` | select | `any` | `any`, `full_time`, `part_time`, `contract`, `temporary`, `internship` |
| `sortBy` | select | `relevance` | `relevance` or `recent` |
| `includeDescription` | boolean | `true` | Include the observed public job description |

#### Example input

```json
{
  "keywords": "Software Engineer",
  "location": "United States",
  "maxResults": 10,
  "datePosted": "any",
  "workplaceType": "any",
  "employmentType": "any",
  "sortBy": "relevance",
  "includeDescription": true
}
```

### Dataset fields

Each Dataset item is one unique public job:

- identity: `job_id`, `title`, `job_url`, `source`, `retrieved_at`;
- company: `company_name`, `company_url`, `company_id`, `industry`;
- job evidence: `location`, `workplace_type`, `employment_type`, `seniority`, `posted_at`, `posted_days_ago`, `applicant_count`, `description`, `apply_url`;
- compensation: `salary_min`, `salary_max`, `salary_currency`, `salary_text`;
- intelligence: `company_jobs_observed`, `company_distinct_roles_observed`, `company_distinct_locations_observed`, `role_cluster`, `hiring_signal_score`, `hiring_signal_level`, `hiring_signal_confidence`, `hiring_signal_reasons`, `warnings`.

`salary_min` and `salary_max` preserve the numeric values and scale displayed publicly. They are not annualized. A bare `$` does not establish an ISO currency, so `salary_currency` remains `null` unless the currency is explicit or the symbol is unambiguous.

### Hiring Signal Score

The company-level score is deterministic and capped at 100:

| Component | Maximum | Rule |
|---|---:|---|
| Observed hiring volume | 45 | 5 points per unique job observed, capped at 45 |
| Recency | 25 | Most recent observed job: ≤1 day 25; ≤3 days 20; ≤7 days 15; ≤30 days 8; older 2; unavailable 0 |
| Role breadth | 15 | 3 points per distinct observed title, capped at 15 |
| Geographic breadth | 10 | 0 locations 0; 1 location 2; 2 locations 5; 3 locations 7; 4+ locations 10 |
| Evidence completeness | 5 | Coverage of public fields across the company's observed records |

Levels are:

- `HIGH`: 70–100 — high observed hiring activity in this run
- `MEDIUM`: 40–69 — moderate observed hiring activity in this run
- `LOW`: 0–39 — limited observed hiring evidence in this run

Confidence is separate from intensity. It is based on public field completeness and date coverage; it does not predict business outcomes.

### Hiring Intelligence

The Actor classifies titles with transparent keyword rules into Engineering, Sales, Marketing, Finance, Operations, Customer Service, Human Resources, Healthcare, Legal, Data, Product, Design, or Other. No external AI model is used.

Company totals, role breadth, location breadth, top companies, top role clusters, top locations, remote share, salary coverage, and date coverage refer only to records observed in the current run. A small `maxResults` intentionally provides a smaller evidence window.

### Use cases

- Recruiting research and sourcing workflows
- Job market research and Labor Market Intelligence
- Competitive hiring intelligence based on observed public postings
- Sales intelligence and Lead Generation Hiring Signals
- Workforce analysis and location-demand snapshots
- Automation with Apify schedules, webhooks, datasets, and integrations
- AI agents and RAG datasets that need structured job evidence

### Output

- **Dataset** — one valid unique job per row, exportable as JSON, CSV, Excel, XML, RSS, or through the Apify API.
- **OUTPUT** — search, filters, status, counts, coverage, rankings, high-signal companies, warnings, methodology, source attempts, and performance.
- **REPORT.html** — search summary, metrics, HIGH/MEDIUM/LOW company counts, top hiring companies, roles, locations, remote share, salary coverage, jobs, score evidence, links, warnings, and methodology.

An empty or blocked search produces a successful empty output with warnings and no billable job events. A failed detail page retains a valid search-card job when its identity, title, company, and public URL remain observable.

### Data integrity

The Actor does not infer missing salaries, currency, applicant counts, workplace type, employment type, seniority, industry, location, or posting date. Unobserved optional values are `null`. Relative public date labels can produce `posted_days_ago`; an exact `posted_at` is emitted only when an exact public date/time attribute is present.

Applicant phrases such as “Be among the first 25 applicants” or “Over 200 applicants” are not treated as exact applicant counts. A public LinkedIn job URL is retained as evidence for every delivered row.

### Limitations

- LinkedIn can change, restrict, rate-limit, localize, or remove its logged-out pages and HTML without notice.
- Results depend on the network region, query, public inventory, ordering, and selected result cap.
- Public search filters and returned fields may vary by country and job.
- A posting can expire between search and detail retrieval.
- Relative dates use deterministic day equivalents (`hours` = 0, `weeks` = 7 days each, `months` = 30 days each); they do not create a fabricated exact timestamp.
- Applicant counts, salary, workplace type, industry, and apply links are often absent.
- This is a point-in-time observation, not a complete census or business forecast.

### Pricing

The prepared Pay Per Event event is `job-result` at **USD 0.0005 per valid unique job delivered**—USD 0.50 per 1,000 results. Empty searches, duplicates, inaccessible pages, discarded rows, errors, warnings, `OUTPUT`, and `REPORT.html` are not billed.

The source package does not activate monetization. During publishing, configure only the custom `job-result` event, disable automatic default-Dataset-item charging to prevent double billing, remove the synthetic start event for a no-start-fee launch, and leave “Pay per event + usage” off.

### Privacy and responsible use

This Actor accesses only public logged-out job pages. It does not access private profiles, collect private emails or phone numbers, use authenticated sessions, solve access challenges, or attempt aggressive access-control evasion. Use public data in accordance with applicable laws, platform terms, privacy obligations, and your legitimate purpose. Do not use results for unlawful discrimination or solely automated high-impact employment decisions.

### Independent product disclaimer

This Actor is independent and is not affiliated with, endorsed by, sponsored by, or officially connected to LinkedIn or Microsoft.

# Actor input Schema

## `keywords` (type: `string`):

Job title, skill, or keyword to search for, such as Python Developer, Sales Manager, or Data Analyst.

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

City, region, or country to search, such as United States, London, or Berlin.

## `maxResults` (type: `integer`):

Maximum number of unique job records to deliver.

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

Limit results by the public posting-age filter.

## `workplaceType` (type: `string`):

Filter by workplace type when LinkedIn exposes the matching public search filter.

## `employmentType` (type: `string`):

Filter by employment type.

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

Order public search results by relevance or recency.

## `includeDescription` (type: `boolean`):

Include the public job description in each Dataset record.

## Actor input object example

```json
{
  "keywords": "Software Engineer",
  "location": "United States",
  "maxResults": 10,
  "datePosted": "any",
  "workplaceType": "any",
  "employmentType": "any",
  "sortBy": "relevance",
  "includeDescription": true
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `output` (type: `string`):

No description

## `report` (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("azzarilabs/linkedin-jobs-hiring-intelligence").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("azzarilabs/linkedin-jobs-hiring-intelligence").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 azzarilabs/linkedin-jobs-hiring-intelligence --silent --output-dataset

```

## MCP server setup

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

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/zOdIvtLthLLqdVrtC/builds/pKJXtGPoluy0b5ibg/openapi.json