# LinkedIn Jobs Scraper - Listings, Company & Type (`flash_scraper/linkedin-jobs-scraper`) Actor
Scrape LinkedIn job postings by keyword & location — title, company, location, work type, parsed salary, employment type, seniority, sector, applicants, recruiter, posted date & job URL. No login, no cookies, no auto-apply. Dedup, filters & a 0-100 job score. Export CSV/JSON/Excel.
- **URL**: https://apify.com/flash\_scraper/linkedin-jobs-scraper.md
- **Developed by:** [Flash Scrape](https://apify.com/flash_scraper) (community)
- **Categories:** Jobs, Social media, Automation
- **Stats:** 4 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: 5.00 out of 5 stars
## Pricing
from $0.000035 / actor start
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/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 Scraper — public job listings with parsed salary, no login, no auto-apply
**Scrape LinkedIn jobs into a clean, analysis-ready spreadsheet.** Give this actor job titles and a location and get back one row per posting: title, company, location, **parsed salary** (min/max/period plus the raw string), employment type, seniority, job function, sector, **applicant count as an integer**, recruiter, posting date, and the job URL — each row topped with a **0–100 job score** so the freshest, best-documented, salary-showing listings sort first. Built for job seekers, recruiters, job-board builders, and labor-market analysts. It reads only **public, logged-out listings** — no LinkedIn login, no cookies, and it never auto-applies. **Pay-per-result**: $5 per 1,000 jobs.
### What it does
- **Searches LinkedIn's public job listings** by keyword and location — postings are fetched through the [`valig/linkedin-jobs-scraper`](https://apify.com/valig/linkedin-jobs-scraper) data provider, with your native filters (date posted, experience level, employment type, remote/on-site/hybrid, specific companies) applied at the source.
- **Parses salaries into numbers**: `"$115,000.00/yr - $120,000.00/yr"` becomes `salaryMin: 115000`, `salaryMax: 120000`, `salaryPeriod: "year"` — and the `minSalary` filter annualizes hourly/monthly pay before comparing.
- **Derives an `isRemote` flag** by scanning the title, location, and description for remote/WFH signals (LinkedIn's own field for this isn't exposed on public listings).
- **Parses applicant counts to integers** so you can filter to low-competition roles with `maxApplicants`.
- **Normalizes and cleans**: posting dates to `YYYY-MM-DD`, tracking parameters stripped from job URLs, whitespace collapsed, duplicates removed across all your search queries.
- **Scores every job 0–100** — salary shown +25, freshness up to +25, full description +20, seniority/sector present +12, company present +8 — and sorts the dataset best-first (ties broken by highest salary).
- **Applies your client-side filters** after cleaning: `requireSalary`, `minSalary` (annualized), `easyApplyOnly`, `maxApplicants`.
### Use cases
- **Job search automation** — schedule a daily run for your target titles, filter to `r86400` (past 24 h) and low applicant counts, and be among the first applicants.
- **Recruiting intelligence** — see which companies are hiring for a role, at what advertised salary, and how much competition each posting draws.
- **Job-board aggregation** — feed a niche job board with clean, deduped LinkedIn postings including salary data where shown.
- **Salary benchmarking** — collect the minority of postings that disclose pay and analyze `salaryMin`/`salaryMax` by title and region.
- **Labor-market research** — track posting volume, seniority mix, and remote share across sectors over time.
### Input
| Field | Type | Default | Description |
|---|---|---|---|
| `searchQueries` | array | `["data analyst"]` | Job titles, skills, or company keywords — one per line. Each is searched separately and results are deduped. |
| `location` | string | `"United States"` | City, state, country, or zip — e.g. `London`, `Remote`. |
| `datePosted` | string | `""` | `r86400` (past 24 h), `r604800` (past week), `r2592000` (past month), or empty for any time. |
| `experienceLevel` | array | `[]` | Seniority codes: `1` Internship, `2` Entry, `3` Associate, `4` Mid-Senior, `5` Director. |
| `contractType` | array | `[]` | Employment type codes: `F` Full-time, `P` Part-time, `C` Contract, `T` Temporary, `I` Internship. |
| `remote` | array | `[]` | Work arrangement codes: `1` On-site, `2` Remote, `3` Hybrid. |
| `companyNames` | array | `[]` | Filter to specific company names. |
| `companyId` | array | `[]` | Filter by LinkedIn numeric company IDs (from a company page URL). |
| `maxItems` | integer | `100` | Max jobs **per search query** (1–1000). |
| `requireSalary` | boolean | `false` | Drop listings that don't show a salary. |
| `minSalary` | integer | `0` | Keep only jobs paying at least this much per year (hourly/monthly pay annualized; 0 = off). |
| `easyApplyOnly` | boolean | `false` | Keep only LinkedIn Easy Apply jobs. |
| `maxApplicants` | integer | `0` | Keep only jobs with at most this many applicants (0 = off). |
```json
{
"searchQueries": ["data analyst"],
"location": "United States",
"datePosted": "r604800",
"experienceLevel": ["3", "4"],
"remote": ["2"],
"maxItems": 100,
"requireSalary": true,
"minSalary": 90000
}
````
### Output
One dataset row per job, deduped across queries and sorted by `job_score`. Export to CSV, JSON, or Excel from the Output tab.
```json
{
"jobTitle": "Data Analyst",
"companyName": "Synechron",
"location": "Pittsburgh, PA",
"jobFunction": "Information Technology",
"isRemote": false,
"salaryRaw": "$115,000.00/yr - $120,000.00/yr",
"salaryMin": 115000,
"salaryMax": 120000,
"salaryPeriod": "year",
"employmentType": "Full-time",
"seniority": "Mid-Senior level",
"sector": "IT Services and IT Consulting",
"applicants": 200,
"applyType": "EXTERNAL",
"applyUrl": "https://careers.synechron.com/...",
"postedDate": "2026-06-21",
"postedTimeAgo": "2 weeks ago",
"recruiterName": null,
"recruiterUrl": null,
"companyUrl": "https://www.linkedin.com/company/synechron",
"jobUrl": "https://www.linkedin.com/jobs/view/4422685636",
"descriptionText": "We are seeking a Data Analyst to…",
"descriptionHtml": "We are seeking a Data Analyst to…
",
"jobId": "4422685636",
"job_score": 69
}
```
### Pricing
This actor uses Apify **pay-per-event pricing: $5 per 1,000 jobs** ($0.005 per result). You're charged only for the cleaned listings delivered **after** dedup and your filters — with `requireSalary` on, listings without pay data cost you nothing. No subscription, no charge for empty runs. Use `maxItems` and the filters to keep cost predictable: a daily 100-job tracked search costs about $0.50/month. The **free Apify plan is enough to try it** on a real search first.
### Tips / FAQ
**Does this apply to jobs for me?** No. It only collects public listings into a dataset — it never logs in and never submits an application, including Easy Apply.
**Do I need a LinkedIn account or cookie?** No. It scrapes the public, logged-out job search — no `li_at` cookie, no credentials, no API key.
**Why is salary usually empty?** LinkedIn shows pay on a minority of postings. The parser fills `salaryMin`/`salaryMax`/`salaryPeriod` whenever a figure appears; use `requireSalary` to keep only those rows.
**What's the difference between `jobFunction` and `isRemote`?** `jobFunction` is the role area LinkedIn lists (e.g. "Information Technology"). Remote status isn't a separate public field, so `isRemote` is derived from remote/WFH keywords in the title, location, and description — treat it as a strong signal, not gospel.
**Where does the data come from, and what if the source is down?** Listings come via an upstream data provider (`valig/linkedin-jobs-scraper`). If one query fails it's skipped and the run continues; on a full transient outage the run ends gracefully with a "temporarily unavailable — please retry" message and **you're charged nothing**.
**Is scraping LinkedIn jobs legal?** This collects **public** job data only, from logged-out pages. LinkedIn rate-limits aggressively and restricts automated access in its terms, so expect occasional partial runs; use the data for personal, research, or recruiting purposes and follow local law.
### Related actors
- [Indeed Jobs Scraper](https://apify.com/flash_scraper/indeed-jobs-scraper) — the same clean-jobs approach for Indeed
- [Multi-Jobboard Scraper](https://apify.com/flash_scraper/multi-jobboard-scraper) — one search across several job boards at once
- [Company & Domain Enricher](https://apify.com/flash_scraper/company-domain-enricher) — turn hiring companies into full firmographic records
**Support:** found a bug or need a feature? Open an Issue on this actor's **Issues tab** — typical response within 1 business day.
# Actor input Schema
## `searchQueries` (type: `array`):
Job titles, skills, or company keywords to search — one per line. e.g. 'data analyst', 'react developer'.
## `location` (type: `string`):
City, state, country, or zip to search within — e.g. 'United States', 'London', 'Remote'.
## `datePosted` (type: `string`):
Only jobs posted within this window.
## `experienceLevel` (type: `array`):
Filter by seniority (leave empty for all).
## `contractType` (type: `array`):
Filter by employment type (leave empty for all).
## `remote` (type: `array`):
Filter by on-site / remote / hybrid (leave empty for all).
## `companyNames` (type: `array`):
Filter to specific company names (leave empty for all).
## `companyId` (type: `array`):
Filter by LinkedIn numeric company IDs (from a company page URL). Leave empty for all.
## `maxItems` (type: `integer`):
Maximum number of jobs to scrape per search query (1-1000).
## `requireSalary` (type: `boolean`):
Drop listings that don't show a salary.
## `minSalary` (type: `integer`):
Keep only jobs paying at least this much per year (hourly/monthly pay is annualized; 0 = no filter).
## `easyApplyOnly` (type: `boolean`):
Keep only jobs that support LinkedIn Easy Apply.
## `maxApplicants` (type: `integer`):
Keep only jobs with at most this many applicants (low-competition roles; 0 = no filter).
## Actor input object example
```json
{
"searchQueries": [
"product manager",
"backend engineer"
],
"location": "United States",
"datePosted": "",
"experienceLevel": [],
"contractType": [],
"remote": [],
"companyNames": [],
"companyId": [],
"maxItems": 100,
"requireSalary": false,
"minSalary": 0,
"easyApplyOnly": false,
"maxApplicants": 0
}
```
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"searchQueries": [
"data analyst"
],
"location": "United States"
};
// Run the Actor and wait for it to finish
const run = await client.actor("flash_scraper/linkedin-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 = {
"searchQueries": ["data analyst"],
"location": "United States",
}
# Run the Actor and wait for it to finish
run = client.actor("flash_scraper/linkedin-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 '{
"searchQueries": [
"data analyst"
],
"location": "United States"
}' |
apify call flash_scraper/linkedin-jobs-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=flash_scraper/linkedin-jobs-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LinkedIn Jobs Scraper - Listings, Company & Type",
"description": "Scrape LinkedIn job postings by keyword & location — title, company, location, work type, parsed salary, employment type, seniority, sector, applicants, recruiter, posted date & job URL. No login, no cookies, no auto-apply. Dedup, filters & a 0-100 job score. Export CSV/JSON/Excel.",
"version": "0.1",
"x-build-id": "EBfxFclRk7tQf8JHf"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/flash_scraper~linkedin-jobs-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-flash_scraper-linkedin-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/flash_scraper~linkedin-jobs-scraper/runs": {
"post": {
"operationId": "runs-sync-flash_scraper-linkedin-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/flash_scraper~linkedin-jobs-scraper/run-sync": {
"post": {
"operationId": "run-sync-flash_scraper-linkedin-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"searchQueries"
],
"properties": {
"searchQueries": {
"title": "Job titles / keywords (what)",
"type": "array",
"description": "Job titles, skills, or company keywords to search — one per line. e.g. 'data analyst', 'react developer'.",
"items": {
"type": "string"
}
},
"location": {
"title": "Location (where)",
"type": "string",
"description": "City, state, country, or zip to search within — e.g. 'United States', 'London', 'Remote'."
},
"datePosted": {
"title": "Date posted",
"enum": [
"",
"r86400",
"r604800",
"r2592000"
],
"type": "string",
"description": "Only jobs posted within this window.",
"default": ""
},
"experienceLevel": {
"title": "Experience level",
"type": "array",
"description": "Filter by seniority (leave empty for all).",
"items": {
"type": "string",
"enum": [
"1",
"2",
"3",
"4",
"5"
],
"enumTitles": [
"Internship",
"Entry level",
"Associate",
"Mid-Senior level",
"Director"
]
},
"default": []
},
"contractType": {
"title": "Employment type",
"type": "array",
"description": "Filter by employment type (leave empty for all).",
"items": {
"type": "string",
"enum": [
"F",
"P",
"C",
"T",
"I"
],
"enumTitles": [
"Full-time",
"Part-time",
"Contract",
"Temporary",
"Internship"
]
},
"default": []
},
"remote": {
"title": "Work arrangement",
"type": "array",
"description": "Filter by on-site / remote / hybrid (leave empty for all).",
"items": {
"type": "string",
"enum": [
"1",
"2",
"3"
],
"enumTitles": [
"On-site",
"Remote",
"Hybrid"
]
},
"default": []
},
"companyNames": {
"title": "Companies",
"type": "array",
"description": "Filter to specific company names (leave empty for all).",
"default": [],
"items": {
"type": "string"
}
},
"companyId": {
"title": "Company IDs",
"type": "array",
"description": "Filter by LinkedIn numeric company IDs (from a company page URL). Leave empty for all.",
"default": [],
"items": {
"type": "string"
}
},
"maxItems": {
"title": "Max jobs per search",
"minimum": 1,
"maximum": 1000,
"type": "integer",
"description": "Maximum number of jobs to scrape per search query (1-1000).",
"default": 100
},
"requireSalary": {
"title": "Only jobs with a salary",
"type": "boolean",
"description": "Drop listings that don't show a salary.",
"default": false
},
"minSalary": {
"title": "Minimum salary (annualized)",
"minimum": 0,
"type": "integer",
"description": "Keep only jobs paying at least this much per year (hourly/monthly pay is annualized; 0 = no filter).",
"default": 0
},
"easyApplyOnly": {
"title": "Easy Apply only",
"type": "boolean",
"description": "Keep only jobs that support LinkedIn Easy Apply.",
"default": false
},
"maxApplicants": {
"title": "Max applicants",
"minimum": 0,
"type": "integer",
"description": "Keep only jobs with at most this many applicants (low-competition roles; 0 = no filter).",
"default": 0
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```