LinkedIn Jobs Scraper - No Login, With Salary & Description

Scrape LinkedIn job search results without logging in, cookies or an account: every job's title, company, location, post date, applicant count, seniority, employment type, job function, industries, salary and full description. Search by keywords and location with LinkedIn's own filters, or paste LinkedIn search links. $1.00 per 1,000 jobs. No proxy needed.

What data you get

One row per job, same columns every time:

• title, companyName, companyUrl, companyLogoUrl, location, url, jobId (stable, use it as a key)
• postedAt (exact to the day), postedText, applicants and applicantsText
• seniorityLevel, employmentType, jobFunction, industries
• Salary: salaryMin, salaryMax, salaryCurrency, salaryPeriod, salaryText, salarySource (LinkedIn's own pay range when it shows one, otherwise the range stated in the description)
• description (full text; descriptionHtml on request)
• Where it came from: searchKeywords, searchLocation, searchUrl, rank

How to use

1. Type job titles or keywords (one per line) and a location, or paste job search links copied from LinkedIn.
2. Optionally filter by date posted, remote/hybrid/on-site, experience level and job type, and pick how many jobs per search.
3. Run it, then download JSON, CSV or Excel, or pull the rows through the API. For a daily feed, schedule it with Only jobs not seen in earlier runs on.

Input

Field	Type	Default	Description
keywords	array	—	One search per line, e.g. data engineer.
location	string	—	Country, region or city, e.g. United States, Berlin. locations (array) searches several.
startUrls	array	[]	LinkedIn job search links (their filters are kept) or job links.
maxJobsPerSearch	integer	100	Per keyword and location, or per link. 0 = as many as LinkedIn lists (up to 1,000).
datePosted	string	any	hour, day, week, month.
workplaceType	array	[]	onsite, remote, hybrid.
experienceLevel	array	[]	internship, entry, associate, mid-senior, director, executive.
jobType	array	[]	full-time, part-time, contract, temporary, internship, volunteer, other.
sortBy	string	relevance	relevance or recent.
fetchDetails	boolean	true	Read each job's page for the description, applicants, seniority, type, function, industries and salary.
includeDescriptionHtml	boolean	false	Keep the description's HTML too.
onlyNew	boolean	false	Leave out (and don't charge) jobs an earlier run already returned.
stateStoreName	string	linkedin-jobs-scraper-state	Where seen jobs are remembered; one name per schedule.
timeoutSecs	integer	30	Per request.
proxyConfiguration	object	off	Not needed; makes job pages faster on very large runs.

Output example

A real row from a run with "keywords": ["data engineer"], "location": "United States" on 2026-09-30 (description shortened here):

{
  "ok": true,
  "type": "job",
  "jobId": "4471769611",
  "url": "https://www.linkedin.com/jobs/view/4471769611/",
  "title": "Enterprise Data- Data Engineer I",
  "companyName": "GBU Life",
  "companyUrl": "https://www.linkedin.com/company/gbu-life",
  "companyLogoUrl": "https://media.licdn.com/dms/image/v2/C4D0BAQFx6MKBZTKkpA/company-logo_100_100/company-logo_100_100/0/1630544932940/gbu_financial_life_logo?e=2147483647&v=beta&t=yqCgevXY2KMtTTe7JwzyXkY0w_8TisysaSklV5gau3I",
  "location": "Pittsburgh, PA",
  "workplaceType": null,
  "postedAt": "2026-09-29T00:00:00.000Z",
  "postedAtApproximate": false,
  "postedText": "12 hours ago",
  "applicants": 106,
  "applicantsText": "106 applicants",
  "seniorityLevel": "Not Applicable",
  "employmentType": "Volunteer",
  "jobFunction": "Information Technology",
  "industries": "Insurance",
  "salaryMin": 82000,
  "salaryMax": 92000,
  "salaryCurrency": "USD",
  "salaryPeriod": "year",
  "salaryText": "$82,000.00/yr - $92,000.00/yr",
  "salarySource": "linkedin",
  "benefits": null,
  "description": "Job Summary\n\nThe Data Engineer is responsible for designing, developing, implementing, and maintaining scalable and reliable data pipelines and infrastructure that support GBU Life’s enterprise data initiatives. This position plays an important role in …",
  "searchKeywords": "data engineer",
  "searchLocation": "United States",
  "searchUrl": "https://www.linkedin.com/jobs/search/?keywords=data%20engineer&location=United%20States",
  "rank": 12,
  "detailed": true,
  "isNew": null,
  "scrapedAt": "2026-09-30T09:13:30.925Z"
}

A search or link that cannot be read comes back as one row with ok: false and a plain reason, such as job not found or no longer open or not a LinkedIn link. Those rows are free.

Pricing

$1.00 per 1,000 jobs ($0.001 each). A job found by two of your searches is written and charged once. Failed inputs, jobs left out by Only jobs not seen in earlier runs, and jobs whose page could not be read (they still come with their search data and a detailError) are free.

Paid Apify plans pay less: 10% off on Bronze, 20% on Silver and 30% on Gold and higher tiers.

Use cases

• Job boards and aggregators: fresh LinkedIn listings for any role and region, with full descriptions.
• Recruiting and sales intelligence: which companies are hiring for what, where, and how many applicants each role draws.
• Salary research: pay ranges by title and location, from LinkedIn's pay ranges and pay-transparency text.
• Job alerts: a daily schedule with Only jobs not seen in earlier runs sends only the new postings to Slack, email or a sheet.

FAQ

Do I need a LinkedIn account or cookies? No. The Actor reads the job pages LinkedIn shows to logged-out visitors. Your account is never involved, so it can't be restricted.

How fast is it? Search results come in pages of 10 in well under a second each. Each job's own page is read at a pace LinkedIn accepts from one IP, about a job a second; turn Read each job's page off for search data only, which is much faster.

Why are some salaries empty? Many postings state no pay. When LinkedIn shows a pay range or the description states one ("between $100,000 and $130,000"), it is filled and salarySource says which.

Does it scrape people? No. It returns job postings and company names only, no profiles or recruiter details.

Is it legal? It reads public job postings anyone can see without logging in. You are responsible for how you use the data.

Integrations

Run it from the Apify API or a client library, schedule it in Apify Console, or connect it to n8n, Make, Zapier or any MCP client through Apify's integrations. Results are available as JSON, CSV, Excel and through the dataset API.

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• Website Contact Extractor: emails, phones and social links from company websites
• Google News Scraper: news about any company, with publisher links

Changelog

• 1.0.0 (2026-09) — first release: keyword and location search with date, workplace, experience and job type filters, search links, job links, full details with salary, new-jobs-only mode.