# LinkedIn Jobs Scraper - Listings, Company & Type (`oriented_wallpaper/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/oriented\_wallpaper/linkedin-jobs-scraper.md - **Developed by:** [Flash Scrape](https://apify.com/oriented_wallpaper) (community) - **Categories:** Jobs, Social media, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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/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 - Listings, Company & Type **This LinkedIn jobs scraper turns any job search into a clean, analysis-ready feed of postings** — one row per job with the title, company, location, parsed salary (min/max/period), employment type, seniority, job function, sector, applicant count, recruiter, posting date, and the job URL. Search LinkedIn's **public** job listings by keyword and location, filter natively (date, seniority, employment type, remote), and export to CSV, JSON, or Excel. Built for **job seekers, recruiters, job-board aggregators, and labor-market analysts**. It reads only **public, logged-out** listings: **no login, no cookies, and it never auto-applies**. --- ### How to scrape LinkedIn jobs 1. Enter one or more **job titles / keywords** (e.g. `data analyst`, `react developer`) and a **location** (e.g. `United States`, `London`). 2. Optionally filter by **date posted**, **experience level**, **employment type**, **remote/on-site/hybrid**, or specific **companies** — these are applied at the source. 3. The scraper pulls matching postings via the proven [`valig/linkedin-jobs-scraper`](https://apify.com/valig/linkedin-jobs-scraper) engine (no LinkedIn login required). 4. Each posting is cleaned: salary parsed into numbers, applicant count parsed to an integer, dates normalized, tracking stripped from URLs. 5. Duplicates are removed, your filters applied, and every job gets a **0–100 job score** so the best-paid, freshest, most complete listings sort to the top. 6. You get a clean table to download as CSV, JSON, or Excel — or pull via API. --- ### What data you get Every row is one job, best-scored first: - **Job title, company, and location.** - **Parsed salary** — `salaryMin`, `salaryMax`, `salaryPeriod`, plus the raw string (LinkedIn shows salary on a minority of posts). - **Employment type and seniority** — e.g. Full-time, Mid-Senior level. - **Job function and sector** — the role area and the company's industry. - **Applicant count** — parsed to an integer so you can spot low-competition roles. - **Recruiter** — name and profile URL when the job poster is shown. - **Posting date** — absolute `postedDate` plus the relative `postedTimeAgo`. - **Apply info** — `applyType` (Easy Apply / external) and `applyUrl`. - **A derived remote flag and a 0–100 job score.** #### Input | Field | Type | Description | |---|---|---| | `searchQueries` | array | Job titles / keywords, one per line. | | `location` | string | City / state / country, or `Remote`. | | `datePosted` | string | Any time / past 24h / past week / past month. | | `experienceLevel` | array | Internship → Director. | | `contractType` | array | Full-time / Part-time / Contract / Temporary / Internship. | | `remote` | array | On-site / Remote / Hybrid. | | `companyNames` | array | Filter to specific companies. | | `maxItems` | integer | Max jobs per search (1–1000). Default `100`. | | `requireSalary` | boolean | Keep only jobs with a salary. | | `minSalary` | integer | Minimum annualized salary (`0` = no filter). | **Example input:** ```json { "searchQueries": ["data analyst", "data engineer"], "location": "United States", "datePosted": "r604800", "experienceLevel": ["3", "4"], "remote": ["2"], "maxItems": 100, "requireSalary": true, "minSalary": 90000 } ```` #### JSON output sample ```json { "job_score": 69, "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://...", "postedDate": "2026-05-21", "postedTimeAgo": "2 weeks ago", "jobUrl": "https://www.linkedin.com/jobs/view/4422685636" } ``` Results render as a clean, sortable table on the Output tab and export to CSV, JSON, or Excel. *** ### Use with AI agents & automation Run this from the Apify **MCP** server so AI agents (Claude, ChatGPT, Cursor) can pull job listings as a tool call, schedule runs via **Make**, **n8n**, or **Zapier** to track new openings or feed a job board, or sync the dataset to **Google Sheets** for a live jobs list. Clean flat JSON drops into pipelines with no glue code. *** ### Pricing **Pay-per-result — you're charged only for the cleaned job listings delivered after your filters.** Set `maxItems` and use filters to keep cost predictable. See the Apify Store page for the current per-result price. *** ### FAQ **Does this apply to jobs for me?** No. It only collects **public** listings into a spreadsheet. It never logs in and never submits applications. **Do I need a LinkedIn account or cookie?** No. It scrapes the public, logged-out job listings — no `li_at` cookie, no credentials. **Why is salary often empty?** LinkedIn only shows salary on a minority of postings. Use `requireSalary` to keep just the ones that show it; the parser fills `salaryMin`/`salaryMax` whenever a figure is present. **Can I search multiple titles?** Yes — pass several `searchQueries`; each is searched with your filters, and results are deduped across searches. **Is scraping LinkedIn legal?** This collects **public** job data only. LinkedIn's User Agreement restricts automated access and LinkedIn rate-limits aggressively, so expect occasional partial runs. Use the data for personal/research/recruiting purposes and follow LinkedIn's terms and local law. **Can I export to CSV or Google Sheets?** Yes — CSV, JSON, or Excel from the Output tab, or sync to Google Sheets via Make, n8n, or Zapier. *** ### Other Flash Scrape scrapers - [Indeed Jobs Scraper](https://apify.com/oriented_wallpaper/indeed-jobs-scraper) — Indeed job listings - [Google Search Results Scraper](https://apify.com/oriented_wallpaper/google-serp-scraper) — Google SERP & rankings - [Amazon Product Scraper](https://apify.com/oriented_wallpaper/amazon-product-scraper) — Amazon products & prices - [Trustpilot Reviews Scraper](https://apify.com/oriented_wallpaper/trustpilot-reviews-scraper) — Trustpilot reviews - [Google Maps Leads Scraper](https://apify.com/oriented_wallpaper/google-maps-leads-opener) — Google Maps business leads - [Yelp Leads Scraper](https://apify.com/oriented_wallpaper/yelp-leads-scraper) — Yelp business leads # 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("oriented_wallpaper/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("oriented_wallpaper/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 oriented_wallpaper/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oriented_wallpaper/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": "e5ekdIMdQlkT0KhTJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oriented_wallpaper~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oriented_wallpaper-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/oriented_wallpaper~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-oriented_wallpaper-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/oriented_wallpaper~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-oriented_wallpaper-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "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 } } } } } } } } } } ```