LinkedIn Jobs Scraper — Jobs, Salaries & Companies at $0.0005/job

Scrape LinkedIn job listings for $0.0005 per job ($0.50 per 1,000 jobs). Search by keyword, location, company, posted date, job type, remote and Easy Apply, then get clean JSON, CSV or Excel with title, company, location, salary range, direct apply URL and the full description. No LinkedIn login, no API key, and you only pay for jobs that actually land in your dataset.

This is the fast, budget tier of our LinkedIn job scrapers. If you need per-URL scraping of individual postings or LinkedIn search URLs with deeper company enrichment, use our premium LinkedIn Jobs Scraper at $0.04/job. If you want the most listings per dollar from keyword searches, this is the one.

Who uses this LinkedIn jobs scraper?

• Recruiters and staffing agencies tracking open roles at target companies and spotting hiring trends before competitors do.
• Job boards and aggregators that need a fresh feed of LinkedIn jobs by keyword and region, deduplicated and ready to publish.
• Sales and lead generation teams who treat a new job posting as a buying signal (a company hiring a Head of Data probably needs data tools).
• Market researchers and analysts studying salary ranges, remote work share and demand for skills by city or industry.
• Job seekers and career coaches building a daily digest of new listings that match a title, radius and posted-within window.
• AI agents and data pipelines that need structured job data on demand through the Apify API or MCP.

What you get: LinkedIn job data fields

Every row in the dataset is one LinkedIn job listing. Fields are omitted from a row when LinkedIn did not provide them.

Field	Description
id	Unique LinkedIn job identifier
title	Job title as posted
company	Hiring company name
location	City, state and country string
job_url	URL of the LinkedIn job posting
job_url_direct	Direct application URL on the company website (needs Fetch Full Descriptions)
description	Full job description in Markdown or HTML (needs Fetch Full Descriptions)
salary_min, salary_max	Salary range when LinkedIn shows one
salary_currency, salary_interval	Currency code and interval (yearly, monthly, hourly)
salary_source	Where the salary came from (direct_data when posted by the employer)
job_type	fulltime, parttime, contract, internship or temporary
is_remote	true when the listing is remote
date_posted	Posting date in ISO format
job_level, job_function, listing_type	Seniority, function and listing type when available
company_industry, company_url, company_url_direct, company_logo, banner_photo_url	Company metadata
emails	Email addresses found in the description, comma separated
skills, experience_range, vacancy_count, work_from_home_type	Extra attributes when LinkedIn exposes them
search_term, matched_search_term	The query that surfaced this row (useful with multi-keyword runs)
scraped_at	UTC timestamp of the run

How to run it: inputs and search modes

You can search with one keyword or up to five at once. Multi-keyword runs are merged and deduplicated so the same job listed under two queries is billed once.

Field	Type	Description
searchTerm	string	Job title or keyword (e.g. "software engineer")
searchTerms	array (max 5)	Multiple queries merged into one dataset
location	string	City, state, or country (e.g. "New York, NY"). Empty = worldwide
maxResults	int (1 to 5000)	Max jobs per search term (default 50)
isRemote	bool	Remote-only filter
jobType	enum	fulltime / parttime / contract / internship / temporary
hoursOld	int	Only jobs posted within the last N hours (24 = last day, 168 = last week)
distance	int	Search radius in miles from the location
offset	int	Skip the first N results for pagination
easyApply	bool	One-click apply jobs only
linkedinFetchDescription	bool	Fetch the full description and direct apply URL (slower, richer)
linkedinCompanyIds	array	Restrict the search to specific LinkedIn company IDs
descriptionFormat	enum	markdown (default) or html
enforceAnnualSalary	bool	Convert hourly and monthly pay to annual
proxyConfiguration	object	Apify proxy (residential recommended)

Example input:

{
  "searchTerms": ["data engineer", "analytics engineer"],
  "location": "Berlin, Germany",
  "maxResults": 200,
  "hoursOld": 168,
  "isRemote": false,
  "linkedinFetchDescription": true,
  "enforceAnnualSalary": true
}

How much does it cost to scrape 1,000 LinkedIn jobs?

Pricing is pay per event. You are billed for jobs delivered to the dataset, never for failed requests, retries or empty searches.

Event	Price
job-scraped (per LinkedIn job returned)	$0.0005
Dataset item (Apify platform fee per stored row)	$0.00001
Actor start (once per run, per GB of memory)	$0.00005

Scheduled change: from September 11, 2026 the price is $0.005 per job ($5 per 1,000).

Worked example for 1,000 jobs on the default 1 GB memory:

• 1,000 × $0.0005 = $0.50 for the jobs
• 1,000 × $0.00001 = $0.01 in dataset storage
• 1 × $0.00005 for the run start
• Total: about $0.51

So 100 jobs cost about $0.05, 1,000 jobs about $0.50, and 10,000 jobs about $5. Set the ACTOR_MAX_TOTAL_CHARGE_USD environment variable (or the spending limit in the Console run options) to hard-cap a run; the scraper reads the cap and truncates the output instead of overspending.

Sample output

A single dataset row with full description fetching enabled looks like this:

{
  "id": "4012345678",
  "title": "Senior Software Engineer",
  "company": "JPMorganChase",
  "location": "New York, NY, US",
  "job_url": "https://www.linkedin.com/jobs/view/4012345678",
  "job_url_direct": "https://careers.example.com/jobs/4012345678",
  "site": "linkedin",
  "job_type": "fulltime",
  "is_remote": false,
  "date_posted": "2026-04-01",
  "salary_source": "direct_data",
  "salary_min": 120000,
  "salary_max": 185000,
  "salary_currency": "USD",
  "salary_interval": "yearly",
  "job_level": "Mid-Senior level",
  "job_function": "Engineering",
  "company_industry": "Financial Services",
  "company_url": "https://www.linkedin.com/company/jpmorganchase",
  "company_logo": "https://media.licdn.com/dms/image/.../logo.png",
  "description": "## About the role\n\nWe are looking for a Senior Software Engineer to join ...",
  "search_term": "software engineer",
  "matched_search_term": "software engineer",
  "scraped_at": "2026-08-29T16:30:00+00:00"
}

Download the whole dataset as JSON, CSV, Excel, XML or RSS from the run page or with one API call.

Frequently asked questions

Does LinkedIn have an official jobs API?

LinkedIn does not offer a public jobs search API. The official Talent Solutions and Job Posting APIs are limited to approved partners and are built for posting jobs, not for reading them. This scraper reads public job listings the same way a logged-out browser does and returns them as structured data, so you can search LinkedIn jobs programmatically without a partnership or an API key.

Do I need a LinkedIn account or API key?

No. The scraper works on public listings without logging in, so your personal LinkedIn account is never involved and cannot be restricted. You only need an Apify account.

How fast is it and are there limits?

A 50-job search usually finishes in under a minute. Runs of 1,000 or more jobs typically take 10 minutes or longer because LinkedIn paginates results and throttles aggressive traffic; raise the run timeout to 3,600 seconds for large sweeps. Each search term can return up to 5,000 jobs and a run can combine up to 5 terms, so one run can deliver up to 25,000 listings. LinkedIn may return fewer results than requested for narrow queries, and you are only charged for what is delivered.

Why do I get fewer jobs than maxResults or zero results?

LinkedIn rate-limits IP addresses after roughly 50 to 100 results per session. Use the default Apify residential proxy group for anything above a handful of pages; the scraper rotates to a fresh IP on each retry automatically. Very narrow filters (for example a small radius plus a short hoursOld window) can also return few jobs. Combining hoursOld with easyApply is not supported on LinkedIn's side, so the scraper drops easyApply and warns in the log.

Can I scrape jobs from specific companies only?

Yes. Pass one or more LinkedIn company IDs in linkedinCompanyIds and the search is restricted to those employers. Combine with hoursOld to monitor a watchlist of companies for new openings every day.

Which output formats are supported?

Results are stored in an Apify dataset and can be exported as JSON, CSV, Excel, XML, HTML table or RSS. Descriptions are returned as Markdown by default, or HTML if you set descriptionFormat to html.

How does this compare to the $0.04/job LinkedIn Jobs Scraper?

This actor is optimised for keyword and location searches at the lowest cost per listing. The premium actor adds scraping by individual job URL and by LinkedIn search URL, at a much higher price per job. Start here for bulk discovery and monitoring, and move up when you need per-URL detail.

Is it legal to scrape LinkedIn jobs?

Scraping publicly available data is generally lawful in many jurisdictions, and job postings are published so that people can find them. You are responsible for how you use the data: respect LinkedIn's terms, applicable privacy laws such as GDPR and CCPA, and avoid storing personal data you do not need. Job listings are business information about employers, not private profile data.

Use with AI agents and MCP

The actor is available as a tool through the Apify MCP server, so Claude, ChatGPT, Cursor and other MCP-capable agents can search LinkedIn jobs on demand:

https://mcp.apify.com/?tools=fetch-actor-details,openclawai/linkedin-jobs-scraper

Add that URL as an MCP server with your Apify token and ask the agent, for example, to "find remote data engineer jobs posted in the last 24 hours and summarise the salary ranges". A step-by-step guide with prompt examples is available on Datapika: https://datapika.com/actors/linkedin-jobs-scraper

Integrations: API, Python, JavaScript and automation

• REST API: start a run with POST https://api.apify.com/v2/acts/openclawai~linkedin-jobs-scraper/runs and read the dataset from GET /v2/datasets/{datasetId}/items?format=json.
• Python: pip install apify-client, then ApifyClient(token).actor("openclawai/linkedin-jobs-scraper").call(run_input={...}).
• JavaScript / Node.js: npm i apify-client, then new ApifyClient({ token }).actor("openclawai/linkedin-jobs-scraper").call({...}).
• Scheduling and webhooks: schedule the actor in Apify Console (for example every morning with hoursOld: 24) and use a webhook to push new rows to your own endpoint.
• No-code: connect through Apify's Zapier, Make, n8n, Google Sheets and Airtable integrations to route new jobs into a spreadsheet, CRM or Slack channel.

Tips for reliable LinkedIn scraping

• Keep the default residential proxy for any run above 100 jobs; datacenter IPs are blocked quickly.
• Use offset to page through large result sets across several runs instead of one very long run.
• Turn on linkedinFetchDescription only when you need the body text or the direct apply URL; it roughly doubles run time.
• Use enforceAnnualSalary when comparing hourly and salaried roles in the same dataset.
• Watch the run log: per-term retry attempts, deduplication counts and any spending-cap truncation are printed there.

Legal and responsible use

This actor collects publicly visible job listings only. It does not log in, does not access private profiles and does not bypass authentication. Use the data in line with LinkedIn's terms of service and the data protection laws that apply to you, and do not use it to spam candidates or employers. If you are unsure about a particular use case, consult a lawyer.