⚡️Rapid Linkedin Jobs Scraper

What does Rapid LinkedIn Jobs Scraper do?

Rapid LinkedIn Jobs Scraper is a cost-effective LinkedIn jobs API and data extraction tool on Apify. It turns public LinkedIn job listings (job titles, companies, locations, posting dates, salaries, full descriptions, seniority, employment type, applicant counts, contact emails and more) into clean, structured data for $0.10 per 1,000 jobs, and you can start free.

Search by job title, location, company, city, posting date or Easy Apply. Then download the results as JSON, CSV, Excel, XML, HTML, RSS or JSONL, or call the Actor from your own app, automation or AI agent through the API.

It is trusted by 15,000+ Apify users, maintained regularly, and runs with a success rate above 99%. Start free: Apify's Free plan includes $5 of monthly credits, enough for up to 50,000 Quick results every month, with no credit card required. You do not need a LinkedIn account, login or cookies.

Built for recruiting, job boards, B2B sales and lead generation, market research, company job monitoring, scheduled (cron) data collection, automation workflows and AI agents.

At a glance

What it is	A cost-effective LinkedIn jobs API and scraper on Apify: a data extraction tool for public LinkedIn job listings
Price	Pay per result. Quick from $0.10 / 1,000 jobs, Detailed from $0.30 / 1,000 jobs. Platform usage and proxies are free
Start free	$5 of free Apify credits every month: up to 50,000 Quick results, no credit card needed
Ways to run	Apify Console, REST API (Python, Node.js, cURL), cron schedules, webhooks, Make / Zapier / n8n and other no-code integrations, MCP for AI agents
Search by	Job titles, location, cities, companies, posting date, Easy Apply
Scrape modes	Quick (search-result data, fastest, $0.10 / 1,000 jobs) and Detailed (adds full job-page data, from $0.30 / 1,000 jobs)
Data you get	23 structured fields: title, company, company logo and link, location, posting date, hiring badges, salary, applicants, full description (text and HTML), seniority, employment type, industries, apply link, contact email
Export formats	JSON, CSV, Excel, XML, HTML table, RSS, JSONL
Reliability	Success rate above 99%, regularly maintained
LinkedIn account needed	No. No login, no cookies, no credentials

Who uses LinkedIn job data, and how?

Public LinkedIn listings show who is hiring, for which roles, where, and at what pay. Teams turn that into data they can filter, chart, enrich and automate. Find your use case and start from the suggested setup.

If you are...	You can...	Suggested setup
A recruiter or staffing agency	Track new openings at target clients, spot companies that are scaling a team, and prioritize roles with the Actively Hiring and Early Applicant flags.	Job titles or company names, Past 24 hours, daily schedule
A job board, newsletter or aggregator	Fill your site with fresh, de-duplicated listings and push only the new jobs into your database.	Detailed, titles and location, Past 24 hours, daily schedule, job_id as the unique key
A B2B sales or lead-generation team	Turn hiring into prospect lists. A company that is hiring is a company that is spending, so look for businesses hiring for roles that signal a need for your product or service.	Detailed, titles that match your ideal customer, Past 24 hours, webhook into your CRM. See the hiring-signal pipeline
A market researcher or analyst	Measure demand for skills, compare roles across cities, track hiring trends over time and benchmark pay with the salary ranges employers publish.	Detailed, several titles and Cities, Past Month
A competitive-intelligence or company-monitoring team	Follow how competitors grow: which teams they expand, which locations they open, how fast they hire and what seniority they look for.	Quick, Company Names, Past 24 hours or Past Week, scheduled run
An AI developer or agent builder	Give your agent or LLM app live job data through the API or MCP.	Detailed for descriptions, see AI agents (MCP)
An automation builder (Make, Zapier, n8n)	Trigger flows when new jobs match, and send them to Google Sheets, Airtable, Slack or your CRM.	Quick for alerts, schedule plus webhook
A job seeker or career coach	Build a personal job-alert workflow: scrape the past 24 hours of roles that match your skills, export them to a spreadsheet and apply while the listing is fresh.	Detailed, your titles and location, Past 24 hours

Why use Rapid LinkedIn Jobs Scraper?

• ✅ Cost-effective. From $0.10 per 1,000 jobs. You pay only for the jobs delivered, platform usage is free and proxies are included, so there is no separate compute or proxy bill.
• ✅ API-first. Run it from the Apify API with ready-made Python and Node.js clients, or from the Console. Add schedules, webhooks and integrations to automate it.
• ✅ Fast and reliable. A success rate above 99%. It is lightweight and does not rely on heavy browser automation, and the Actor is maintained regularly.
• ✅ Rich output. 23 structured fields per job, including salary, applicants, full description (text and HTML), seniority, industries and contact email.
• ✅ Advanced filters. Combine job titles, location, cities, companies, posting time and Easy Apply.
• ✅ Many searches in one run. Search several job titles, cities and companies together; the results are merged into a single dataset.
• ✅ Duplicate-free results. A job found by more than one search is returned only once.
• ✅ Freshness signals. Every job carries its posting date plus LinkedIn's hiring badges, so you can sort by recency and urgency.
• ✅ Two scrape modes. Pick Quick for fast, low-cost listing data or Detailed for full job-page data. You pay only for the depth you need.
• ✅ Multiple export formats. JSON, CSV, Excel, XML, HTML, RSS and JSONL, or read the dataset through the API.
• ✅ Pay per result, with a spending cap. The run stops as soon as your maximum cost per run is reached and keeps everything collected so far.
• ✅ No LinkedIn account needed. Nothing to authenticate, no cookies to manage, no CAPTCHAs to solve, and no risk to your own LinkedIn account.
• ✅ Full Apify platform power. Scheduling, API access, webhooks, integrations, run monitoring and data storage are all included.

Rapid LinkedIn Jobs Scraper vs other ways to get LinkedIn job data

Approach	LinkedIn login needed	Risk to your account	Practical scale	Setup
Manual copy and paste	No	None	A few dozen jobs	Hours of repetitive work
Login-based scrapers and browser extensions	Yes (your account or cookies)	Account restrictions are possible	Medium	Install, log in, keep sessions alive
LinkedIn's official job APIs	Partner approval	None	Restricted to approved partners	Application and approval process
Rapid LinkedIn Jobs Scraper	No	None. It never touches your account	Large runs with multiple titles, cities and companies	Fill in a form and click Start, or call the API

What data can you extract from LinkedIn jobs?

Every job comes back as one structured record with up to 23 fields. The data falls into six groups:

• Job basics: job ID, title, location and the direct LinkedIn job URL.
• Company: company name, LinkedIn company page and company logo.
• Freshness and hiring signals: posting date, relative posting time, and the Actively Hiring and Early Applicant badges.
• Compensation and competition: published salary range and the number of applicants.
• Job content: the full description as plain text and as raw HTML, plus seniority level, employment type and industries.
• Application details: the apply link, whether Easy Apply is available, and the first contact email found in the description.

Quick vs Detailed: which scrape mode should you choose?

The Scrape Mode input controls how much data is collected for each job. Both modes accept exactly the same search filters.

	Quick (quick)	Detailed (detailed, default)
Data collected	Everything shown on LinkedIn's job search results: title, company, location, posting date, hiring badges and job link	Everything in Quick, plus data from each job's own page: description, seniority, employment type, industries, applicants, salary and apply link
Speed	Fastest, because individual job pages are not opened	Slower, because every job page is opened
Price	$0.10 / 1,000 jobs	$0.30 to $0.35 / 1,000 jobs, depending on your Apify plan
Best for	Monitoring new postings, building alerts, counting and trend analysis	Lead generation, salary research, candidate matching, AI and NLP on job descriptions

How to scrape LinkedIn jobs (step by step)

1. Open the Actor on Apify Store and click Try for free (create a free Apify account if you don't have one).
2. Add your job titles (for example Software Engineer, Data Scientist). Each title is searched separately and the results are merged.
3. Set the location. Use a country, region or city such as United States, Europe or San Francisco, CA.
4. Choose a scrape mode (Quick or Detailed) and add optional filters: posting time, companies, cities or Easy Apply.
5. Set how many jobs you want in Number of Jobs Entries (minimum 10).
6. Click Start. Results appear in the dataset while the run is still going.
7. Download your data from the Output tab as JSON, CSV, Excel, XML, HTML, RSS or JSONL, or fetch it through the API.

Example inputs

Python jobs from the past week, with full details

{
  "jobs_titles": ["Python Developer", "Backend Engineer"],
  "location": "United States",
  "posted_within": "Past Week",
  "jobs_entries": 500,
  "scrape_mode": "detailed"
}

New jobs at specific companies, from the last 24 hours (fast monitoring)

{
  "company_names": ["Stripe", "Shopify", "Atlassian"],
  "posted_within": "Past 24 hours",
  "jobs_entries": 200,
  "scrape_mode": "quick"
}

Data analyst jobs across several UK cities

{
  "jobs_titles": ["Data Analyst"],
  "location": "United Kingdom",
  "cities": ["London", "Manchester", "Birmingham"],
  "jobs_entries": 300
}

Hiring signals for a sales team: companies hiring sales roles in the last 24 hours

{
  "jobs_titles": ["Sales Development Representative", "Account Executive"],
  "location": "United States",
  "posted_within": "Past 24 hours",
  "jobs_entries": 500,
  "scrape_mode": "detailed"
}

How much does it cost to scrape LinkedIn jobs?

Rapid LinkedIn Jobs Scraper uses pay-per-result pricing. Quick jobs cost $0.10 per 1,000 results (one hundredth of a cent per job) on every plan, and Detailed jobs cost $0.35 per 1,000 results on the Free plan, falling to $0.30 per 1,000 on the Business plan. Apify platform usage is free, so there are no separate compute or proxy charges. You pay only for the jobs delivered to your dataset.

Result type	Free	Starter (Bronze, 4% off)	Scale (Silver, 5% off)	Business (Gold, 9% off)
Quick job	$0.10 / 1,000	$0.10 / 1,000	$0.10 / 1,000	$0.10 / 1,000
Detailed job	$0.35 / 1,000	$0.34 / 1,000	$0.32 / 1,000	$0.30 / 1,000

Cost examples

Jobs scraped	Quick	Detailed (Free plan)	Detailed (Business plan)
1,000	$0.10	$0.35	$0.30
10,000	$1.00	$3.50	$3.00
50,000	$5.00	$17.50	$15.00
100,000	$10.00	$35.00	$30.00

Is there a free plan?

Yes. Every Apify account on the Free plan gets $5 of free platform credits each month. With Quick mode that is up to 50,000 LinkedIn jobs per month for free, and about 14,000 jobs per month in Detailed mode. No credit card is needed to get started.

How do I stay in control of my spending?

Set a maximum cost per run in the run options. The Actor stops as soon as the limit is reached and keeps every result collected up to that point, so you never pay more than you planned.

For full pricing details, see the Pricing tab.

Input

Open the Input tab to configure a run. Only the fields you fill in are applied, so you can start with just a job title and a location. Here is the form, followed by what each field does:

🧾 Input fields

Title	ID	Type	Description
Job Titles	jobs_titles	List of strings	Job titles to search for, for example "Python Developer" or "Data Scientist". Each title is searched separately and the results are merged.
Location	location	String	A broad location such as Worldwide, Europe or Canada, or a full location string such as San Francisco, CA.
Cities (optional)	cities	List of strings	Specific cities to search, for example "London" and "Manchester". Set Location to the country so the cities are matched correctly.
Company Names	company_names	List of strings	Only return jobs from these companies. Use each company's name as it appears on LinkedIn for the best match.
Number of Jobs Entries	jobs_entries	Integer	How many jobs you want in this run (minimum 10, default 1,000).
Scrape Mode	scrape_mode	quick or detailed	How much data to collect for each job. detailed is the default. See Quick vs Detailed.
Job Posting Time	posted_within	Select	Any Time, Past 24 hours, Past Week or Past Month. If you set it, it takes priority over Custom Job Post Time.
Custom Job Post Time	job_post_time	String	A custom posting window in seconds, prefixed with r. For example r86400 is the past day and r172800 is the past two days. It is used only when Job Posting Time is not set.
Easy Apply Only	easy_apply	Boolean	Return only jobs that offer LinkedIn's "Easy Apply".

Good to know

• Company searches cover all regions by default. If you filter by company and leave Location empty, jobs are collected worldwide. Add a location to focus on one country.
• Existing integrations keep working. The older field name job_title is still accepted. Integrations that still send the removed experience, employment_type or work_arrangement fields (or their older names experience_level, job_type and work_schedule) keep running: those fields are accepted and ignored. For new integrations, use the fields in the table above.
• Every filter is optional. Leave a filter empty to include all values for it.

Output

Each run stores its results in an Apify dataset. You can download the dataset in various formats such as JSON, HTML, CSV, Excel, XML, RSS or JSONL, preview it as a table in the Output tab, or read it through the API.

📤 Output Fields

The Mode column shows which scrape mode returns the field. Fields that LinkedIn does not show for a particular job are returned as null.

Title	ID	Type	Mode	Description
Job ID	job_id	String	Quick, Detailed	Unique LinkedIn identifier of the job post.
Job URL	job_url	String	Quick, Detailed	Direct link to the job on LinkedIn.
Search Keyword	search_keyword	String	Quick, Detailed	The job title from your list whose search found this job.
Job Title	job_title	String	Quick, Detailed	Title of the position.
Company Name	company_name	String	Quick, Detailed	Name of the hiring company.
Company URL	company_url	String	Quick, Detailed	Link to the company's LinkedIn page, when available.
Company Logo URL	company_logo_url	String	Quick, Detailed	Direct URL of the company logo image.
Location	location	String	Quick, Detailed	Where the job is located.
Time Posted	time_posted	String	Quick, Detailed	How long ago the job was posted, for example "2 days ago".
Posted Date	posted_date	String	Quick, Detailed	Posting date in ISO format, for example 2026-09-24.
Hiring Badge	hiring_badge	String	Quick, Detailed	The badge LinkedIn shows on the listing, such as "Actively Hiring" or "Be an early applicant".
Actively Hiring	is_actively_hiring	Boolean	Quick, Detailed	true when the listing carries the "Actively Hiring" badge.
Early Applicant	is_early_applicant	Boolean	Quick, Detailed	true when the listing carries the "Be an early applicant" badge.
Apply URL	apply_url	String	Detailed	The link to apply. LinkedIn no longer exposes employers' external application links, so this is the LinkedIn job page, where the Apply button is.
Number of Applicants	num_applicants	String	Detailed	Current number of applicants, as shown by LinkedIn.
Salary Range	salary_range	String	Detailed	Salary or compensation, when the employer publishes it.
Job Description	job_description	String	Detailed	The full job description as plain text.
Job Description HTML	job_description_raw_html	String	Detailed	The full job description as raw HTML, with its original formatting.
Seniority Level	seniority_level	String	Detailed	The seniority level LinkedIn lists for the job.
Employment Type	employment_type	String	Detailed	Full-time, part-time, contract and so on.
Industries	industries	String	Detailed	The industries related to the job.
Easy Apply	easy_apply	Boolean	Detailed	Whether LinkedIn Easy Apply is available for the job.
Contact Email	contact_email	String	Detailed	The valid email address found in the job description, if the poster included one.

🧪 Sample output

Tips for getting the best LinkedIn job data

• Start with Quick, then go deeper. Use Quick to discover and count jobs cheaply, then run Detailed when you need descriptions, salaries or applicant numbers.
• Collect larger volumes by widening the search. The number of jobs a search can return depends on how many public listings LinkedIn shows for it. To collect more, add several related job titles, list multiple cities, add company names, or leave optional filters.
• Monitor new jobs with a schedule. Combine Past 24 hours with a daily Apify schedule to receive only fresh postings.
• Match location to your goal. Use a country in Location with a list of Cities for city-by-city coverage, or a broad region such as Europe for wide coverage.
• Use exact company names. Company filters work best with the name exactly as it appears on the company's LinkedIn page.
• Use the Easy Apply filter to narrow quickly. It returns only jobs that support LinkedIn's one-click application.
• Cap your spend. Set a maximum cost per run, especially for large Detailed runs.
• Use results while the run is in progress. Jobs are added to the dataset as they are collected, so you can start using them before the run finishes.
• Expect empty fields for some jobs. Salary, applicant count and contact email only appear when the listing provides them.

Integrations, scheduling and API access

Schedule runs and get alerts

Run the scraper on any cron timing (hourly, daily, weekly or a custom cron expression) with Apify schedules, and trigger webhooks when a run finishes to notify your team or kick off the next step in your pipeline.

Connect LinkedIn job data to your tools

Send results to Google Sheets, Airtable, Slack, Make, Zapier, n8n and more using Apify's integrations. Build job alerts, CRM enrichment flows and reporting dashboards without writing code.

Run the scraper through the API

You can integrate the scraper directly into your database, app or automation pipeline with the Apify API. Replace YOUR_APIFY_API_TOKEN with your token from the Apify Console.

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_API_TOKEN' });

const input = {
    jobs_titles: ['Software Engineer', 'Data Scientist'],
    location: 'United States',
    posted_within: 'Past 24 hours',
    jobs_entries: 100,
    scrape_mode: 'detailed',
};

// Run the Actor and wait for it to finish
const run = await client.actor('worldunboxer/rapid-linkedin-scraper').call(input);

// Fetch the results from the run's dataset
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Scraped ${items.length} LinkedIn jobs`);

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run_input = {
    "jobs_titles": ["Software Engineer", "Data Scientist"],
    "location": "United States",
    "posted_within": "Past 24 hours",
    "jobs_entries": 100,
    "scrape_mode": "detailed",
}

# Run the Actor and wait for it to finish
run = client.actor("worldunboxer/rapid-linkedin-scraper").call(run_input=run_input)

# Fetch the results from the run's dataset
for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(job["job_title"], "-", job["company_name"])

cURL

curl -X POST "https://api.apify.com/v2/acts/worldunboxer~rapid-linkedin-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"jobs_titles": ["Product Manager"], "location": "London", "jobs_entries": 50, "scrape_mode": "quick"}'

See the API tab of this Actor for ready-made code in more languages and the full API reference.

Turn LinkedIn hiring signals into leads and alerts

Hiring is one of the clearest public signals that a company is investing, expanding or shopping for tools and services. Because the Actor runs on a schedule and can call a webhook when it finishes, you can wire it into a repeatable pipeline without building or maintaining a scraper.

Hiring-signal pipeline for sales and lead generation

Step	Tool	What happens
1. Collect	This Actor on a daily Apify schedule	Searches for job titles that signal a need for your product, limited to Past 24 hours.
2. Trigger	An Apify webhook or a Make, Zapier or n8n trigger	Fires when the run finishes and hands the dataset to the next step.
3. Filter	Make, Zapier, n8n or your own code	Keeps the jobs that match your ideal customer, for example by location, industries or keywords in job_description.
4. Enrich and draft (optional)	Your enrichment tool and an LLM	Finds the right decision-maker and drafts an opening line that mentions the open role.
5. Deliver	Google Sheets, your CRM, a sales-engagement tool or Slack	New prospects and alerts land where your team already works.

Useful fields for this flow: company_name, company_url, job_title, posted_date, location, industries, hiring_badge and job_url. contact_email is filled only when the poster published an address in the description, so pair the pipeline with your own enrichment to reach the right person, and treat email addresses as personal data (see Legal and compliance).

Start from the hiring signals for a sales team input in Example inputs.

More workflow patterns

Pattern	Setup
Company job monitoring	Company Names with Past 24 hours, Quick mode, a daily schedule, and a webhook or Slack integration that posts the new jobs.
Niche job board feed	Detailed mode with Past 24 hours on a daily schedule; upsert the results into your database using job_id as the key.
Hiring-trend tracker	A weekly run with several job titles and Cities, appended to Google Sheets or your database so you can chart week-over-week demand and salaries.
Personal job alerts	Your titles and location with Past 24 hours, sent to a spreadsheet, Slack or email every morning.

Use LinkedIn job data with AI agents (MCP)

Connect this scraper to your AI assistant through the Apify MCP server and let it search LinkedIn jobs on demand, with live data instead of stale training data. There is no LinkedIn account or cookie to configure, which keeps agent setups simple.

Works with: Claude Desktop, Claude.ai, Claude Code, Cursor, ChatGPT, Codex CLI, VS Code, Antigravity, Kiro and any other MCP-compatible client.

Set up in three steps

1. Open the configurator above and choose your AI client.
2. Connect it to your Apify account by following the instructions the configurator gives you for that client.
3. Ask in plain language. Mention worldunboxer/rapid-linkedin-scraper so the assistant picks this Actor, and say whether you want Quick (counts and listings) or Detailed (descriptions, salary and applicants).

Runs started by your assistant are billed to your Apify account at the same pay-per-result prices as any other run.

Example prompts you can copy

Use the copy button on any prompt, then paste it into your AI assistant.

Job seekers and career coaches

Use the Apify MCP connector to run worldunboxer/rapid-linkedin-scraper in Detailed mode. Find 50
DevOps jobs in Germany posted in the last 24 hours that have Easy Apply, extract the key
requirements and compare them against my attached resume. Rank the roles by fit.

Find data analyst jobs in Toronto posted this week, list the 10 skills they ask for most, and draft
a tailored opening paragraph for the three best matches.

Recruiters and staffing agencies

Find 50 Python developer jobs in the United Kingdom posted in the last 24 hours and summarize the
salary ranges.

List companies in London that are actively hiring data analysts, and group them by industry.

Sales and lead generation

Find companies in the United States hiring Sales Development Representatives in the past 24 hours.
Group the jobs by company and suggest a one-line outreach angle for each one.

Analysts and competitive intelligence

Compare the number of senior product manager openings in Berlin, Amsterdam and Paris this month.

Check Stripe, Shopify and Atlassian for jobs posted in the last 24 hours and summarize which teams
are hiring.

Frequently asked questions

Do I need a LinkedIn account or cookies to scrape LinkedIn jobs?

No. Rapid LinkedIn Jobs Scraper collects only the job information LinkedIn shows publicly to visitors who are not logged in. You never enter credentials or cookies, so your own LinkedIn account is never involved and cannot be restricted.

Is it legal to scrape LinkedIn jobs?

Collecting publicly available information is generally allowed in many jurisdictions, but the rules depend on your country, how you use the data, and LinkedIn's Terms of Service. Job listings can also contain personal data, which is regulated by laws such as the GDPR. This is not legal advice. See Legal and compliance below and consult a lawyer if you are unsure.

How much does the LinkedIn jobs scraper cost?

Quick jobs cost $0.10 per 1,000 results, and Detailed jobs cost from $0.30 per 1,000 results, depending on your Apify plan. Platform usage is free. See How much does it cost and the Pricing tab for details.

What is the most cost-effective way to scrape LinkedIn jobs?

Use pay-per-result pricing where you are charged only for jobs delivered. Here, Quick jobs cost $0.10 per 1,000 on every plan and Detailed jobs cost from $0.30 per 1,000. Proxies and platform usage are included at no extra charge, the Free plan covers up to 50,000 Quick results per month, and a maximum cost per run caps your spend. Start with Quick to discover and count jobs, then run Detailed only where you need descriptions, salaries or applicant counts.

Can I scrape LinkedIn jobs for free?

Yes, within limits. Apify's Free plan includes $5 of platform credits every month, enough for up to 50,000 Quick results (about 14,000 Detailed results) each month, with no credit card required.

How many jobs can I scrape in one run?

Set the number you want in Number of Jobs Entries and the Actor collects up to that many unique jobs. The exact number depends on how many public listings LinkedIn shows for your search: a very narrow search has fewer jobs than a broad one. To collect large volumes, combine several job titles, cities and companies, and keep filters broad.

Which scrape mode should I use, Quick or Detailed?

Use Quick when listing-level data (title, company, location, posting date, hiring badges, link) is enough, for example to monitor new postings. Use Detailed when you need descriptions, seniority, employment type, industries, applicant counts, salary or contact email. Detailed is the default.

What data does the LinkedIn jobs scraper extract?

Job ID and URL, title, company name, company page and logo, location, posting date, hiring badges, salary range, number of applicants, full description (text and HTML), seniority level, employment type, industries, Easy Apply status, apply link and contact email, 23 fields in total. Quick mode returns the listing-level fields and Detailed mode returns all of them. See Output Fields.

Can I scrape LinkedIn jobs by company?

Yes. Add company names to Company Names and the scraper returns jobs posted by those companies. Combine it with a location, job title or posting-time filter to narrow the results.

Can I scrape jobs from several cities or countries at once?

Yes. Put the country in Location and list the cities in Cities, or use a broad region such as Europe or Worldwide. Results from every search are merged and de-duplicated.

Can I get LinkedIn salary data?

Yes, when the employer publishes it. In Detailed mode the salary_range field contains the compensation shown on the listing. Many LinkedIn jobs do not list a salary, and those return null.

Does it extract recruiter or contact emails?

In Detailed mode, contact_email contains the first email address found in the job description, when the poster included one. The Actor does not access private profiles or messages. Treat any email addresses as personal data and see Legal and compliance.

How can I monitor new LinkedIn jobs automatically?

Set Job Posting Time to Past 24 hours and run the Actor on a daily Apify schedule. Add a webhook or an integration to send the new jobs to Google Sheets, Slack, your database or your own app.

Can I run the LinkedIn jobs scraper on a schedule or cron?

Yes. Apify schedules use cron expressions, so you can run the Actor every hour, every day, every week or on any custom timing, with no server of your own. Pair a schedule with a webhook to process the results as soon as each run finishes.

How do I monitor a competitor's LinkedIn job postings?

Add the competitor's name to Company Names, set Job Posting Time to Past 24 hours or Past Week, and run it on a schedule in Quick mode. Send each run's results to Slack, Google Sheets or your database with a webhook or integration to see which roles, teams and locations they are hiring for.

How do I use LinkedIn job postings as hiring signals for sales leads?

Run the Actor daily with Past 24 hours and job titles that signal a need for your product or service. Use company_name, company_url, job_title, posted_date and location to build a prospect list, then send it to your CRM or outreach tool. See the hiring-signal pipeline.

How do I export LinkedIn jobs to Google Sheets?

Download the dataset as CSV or Excel and open it in Google Sheets, or use Apify's Google Sheets integration to send each run's results to a spreadsheet automatically. For a spreadsheet that stays current, add a daily schedule.

Can I scrape LinkedIn jobs with Python or Node.js?

Yes. Use the Apify API client for Python or JavaScript to start a run and read the dataset in a few lines of code. Copy-paste examples are in Run the scraper through the API, and the API tab has more languages.

Can I connect LinkedIn job data to Make, Zapier or n8n?

Yes. Apify integrates with Make, Zapier and n8n, so you can start the Actor or react when a run finishes, then map fields such as job_title, company_name, location and job_url to Google Sheets, Airtable, Slack, a CRM or any other app.

Can I build a job board with LinkedIn job data?

Yes. Run the Actor on a daily schedule with Past 24 hours in Detailed mode and load the results into your database, using job_id to avoid duplicates. Check LinkedIn's Terms of Service and the laws that apply to you before republishing job data, as described in Legal and compliance.

How fast is the scraper?

It is lightweight and does not rely on heavy browser automation. Quick mode typically returns hundreds of jobs in seconds. Detailed mode takes longer because it opens each job page, and larger runs scale accordingly.

How reliable is the scraper?

It runs with a success rate above 99% and is maintained regularly. If a run does not behave as expected, open the Issues tab and describe the problem.

Why did I get fewer jobs than I asked for?

The number of jobs available depends on how many public listings LinkedIn shows for your search. Narrow combinations of title, location and filters have fewer results. Try broader filters, a longer posting window, additional job titles, more cities, or a broader location.

Why are some fields empty?

Fields such as salary, applicant count and contact email are returned only when the listing provides them. Fields LinkedIn does not show for a job come back as null.

Can I export LinkedIn jobs to Excel, CSV or JSON?

Yes. Download the dataset as Excel, CSV, JSON, XML, HTML, RSS or JSONL from the Output tab, or read it through the API.

Can I use this with ChatGPT, Claude or other AI tools?

Yes. The Actor works with the Apify MCP server, so MCP-compatible tools such as Claude, ChatGPT and Cursor can run it directly. See Use LinkedIn job data with AI agents for setup steps and example prompts.

Is there a LinkedIn jobs API, and is this an alternative?

LinkedIn's official job APIs are limited to approved partners. Rapid LinkedIn Jobs Scraper is a LinkedIn Jobs API alternative that gives you structured, programmatic access to public LinkedIn job data through the Apify API, with no partner approval and no LinkedIn account.

What happens if my run reaches its spending limit?

The Actor stops and keeps every result collected so far, so your dataset stays usable and you never pay more than the maximum cost per run you set.

Where can I report a problem or request a feature?

Open the Issues tab of this Actor, or use the contact details in Support and feedback. Bug reports and ideas are welcome.

Start scraping LinkedIn jobs

Add a job title and a location, click Start, and watch the jobs arrive in your dataset while the run is still going. It is free to start, with $5 of Apify credits every month and no credit card.

Legal and compliance

This Actor is built to be ethical. It collects only information that LinkedIn shows publicly to visitors who are not logged in, and it does not access private profiles, messages or account data. We therefore believe it is safe to use for ethical purposes.

However, be aware that your results can contain personal data. For example, a job description may include a contact name or email address that the poster chose to publish (returned in contact_email). Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not collect or use personal data unless you have a legitimate reason to do so. If you are unsure whether your reason is legitimate, consult your lawyers.

LinkedIn's Terms of Service place restrictions on automated data collection. Review them and make sure your use case complies with them and with the laws that apply to you. You are responsible for how you use the data.

Rapid LinkedIn Jobs Scraper is an independent tool and is not affiliated with, endorsed by or sponsored by LinkedIn. LinkedIn is a trademark of its respective owner.

More job scrapers

Need job data from other sources? Try these Actors:

Actor	What it does
Rapid Indeed Jobs Scraper	Extract Indeed job listings from dozens of countries, with salaries, company profiles, contact emails, descriptions and apply links.
Dice Jobs Scraper	Extract job listings, company details, salaries and full job descriptions from Dice.com.

Support and feedback

Rapid LinkedIn Jobs Scraper is built and maintained by Umesh Patidar. If you like it, a review helps others find it. For bugs, ideas or custom requirements:

• Issues tab: report problems or request features directly on this Actor's page.
• Feedback form: https://forms.gle/HQyJGukRrCKmf2qF8
• Email: umeshpatidar.dev@gmail.com
• LinkedIn: Umesh Patidar