LinkedIn Jobs Scraper

Scrape LinkedIn job listings by keyword, location, work type, and date posted. No login, no cookies, no browser — just fast, structured job data with harvestapi-compatible field names for drop-in migration.

Built for sales, recruiting, and outreach teams who need fresh LinkedIn jobs data without managing logins, proxies, or parsers.

What it does

• Search LinkedIn's public job listings by keyword, location, work arrangement, and recency.
• No login required. Uses LinkedIn's public guest endpoints — no account, no li_at cookie, no ban risk.
• Full descriptions + structured detail. Optionally fetch each job's detail page for the complete description plus applicant count, seniority, employment type, job function, industries, and company URL.
• harvestapi-compatible output. Field names match harvestapi~linkedin-job-search, so you can swap this actor in without rewriting downstream code.
• Salary parsing. When LinkedIn exposes compensation, it is parsed into salary_low / salary_high.

Pricing

Pay-per-event. $0.50 per 1,000 results — you pay only for jobs written to the dataset.

Event	Price	Charged per
Job scraped (job-scraped)	$0.50 / 1,000	Each job written to the dataset
Actor start (apify-actor-start)	$0.00005	Per run (first 5 seconds of compute waived)

Cost examples: 200 jobs ≈ $0.10 · 1,000 jobs ≈ $0.50 · 1,500/month (daily monitoring) ≈ $0.75/month. Cap total spend per run with Max cost per run in the run settings.

Input

Field	Type	Default	Description
keywords	string	required	Job title or keywords (e.g. "Director of AI").
location	string	"United States"	City, state, or country.
workType	enum	"any"	remote / hybrid / onsite / any.
datePosted	enum	"past_week"	past_24h / past_week / past_month / any.
maxResults	integer	100	Cap on records returned (1–1000).
fetchFullDescription	boolean	true	Fetch each job's detail page for full description and structured detail fields (one request per job).
proxyConfig	object	{}	Apify proxy settings. Datacenter proxy is sufficient; residential recommended for very large runs.

Example input

{
  "keywords": "Director of AI",
  "location": "United States",
  "workType": "remote",
  "datePosted": "past_week",
  "maxResults": 50,
  "fetchFullDescription": true
}

Output

Each dataset record (harvestapi-compatible field names):

{
  "id": "4424285589",
  "title": "Director of Artificial Intelligence",
  "companyName": "Akerman LLP",
  "companyUrl": "https://www.linkedin.com/company/akerman",
  "companyLogo": "https://media.licdn.com/.../company-logo.png",
  "location": "Miami, FL",
  "postedAt": "2026-06-04T00:00:00.000Z",
  "applicantCount": 25,
  "salary": null,
  "salary_low": null,
  "salary_high": null,
  "seniorityLevel": "Director",
  "employmentType": "Full-time",
  "jobFunctions": ["Engineering", "Information Technology"],
  "industries": ["Law Practice"],
  "descriptionText": "Full job description text...",
  "link": "https://www.linkedin.com/jobs/view/...-4424285589",
  "applyUrl": null,
  "workplaceTypes": ["On-site"],
  "workRemoteAllowed": false,
  "source_platform": "linkedin",
  "scraped_at": "2026-06-12T12:00:00Z"
}

Detail fields (companyUrl, companyLogo, applicantCount, seniorityLevel, employmentType, jobFunctions, industries, descriptionText) are populated when fetchFullDescription is true.

Notes

• Applicant count is null when LinkedIn hides it. Salary is null on most listings — LinkedIn structurally omits compensation from the majority of public postings.
• applyUrl is present for harvestapi compatibility but is often null — LinkedIn resolves the apply link client-side.
• Respect LinkedIn's terms and applicable law in your jurisdiction when using scraped data.

Use with AI agents (MCP)

This actor is callable from any MCP-compatible AI agent (Claude, Cursor, and others) through Apify's hosted MCP server. Add it as a tool by its actor ID:

Mfrostbutter/linkedin-jobs-scraper

Point your MCP client at https://mcp.apify.com (authenticated with your Apify token) and the agent can run LinkedIn job searches on demand. The input schema is fully described, so the agent fills keywords, location, workType, and datePosted without extra prompting.

Compatibility

Output field names match harvestapi~linkedin-job-search at the base level, so existing pipelines built against that actor work without changes.