LinkedIn Jobs Scraper — Short Output

Claude / Codex skill to describe and setup this actor: SKILL.md

Extract live job postings from LinkedIn's public job search into compact JSON — no account, cookies, browser, or start fee. Filter by keyword, location, remote-only, and posting age.

Choose this Actor for a lighter retrieval path. LinkedIn Jobs Scraper — FULL (All Info) reads the public detail page as well; both publish the same shared flat fields, with mapped source identifiers in custom.data.

Built for scheduled monitoring and job-alert bots: run it on a cron and compare each full current snapshot downstream to detect new postings.

Why this scraper

	This Actor	Typical alternatives
Price per 1,000 jobs	$0.90	$0.40–$10.00
Start fee per run	$0	up to $0.005–$0.05 — adds up fast on scheduled runs
Login / cookies / proxy	None needed	some require cookies or proxies
Zero-result runs	Free + diagnostic row	often billed or fail silently
Max jobs per run	~200 (honest guest-API limit)	up to 1,000 for bulk scrapers

If you need 1,000+ postings in one bulk pull, a large-scale scraper serves you better. For frequent LinkedIn polling, the Actor returns a current snapshot that your alerting system can compare with prior results.

What LinkedIn jobs data does this scraper extract?

Published postings follow nomad-agent-job-row-v1. The shared fields carry source identity, title and company, parsed locations, dates, the complete plain-text body when available, source markup when usable, and work type. Unavailable scalar facts are null; no parsed location is []. Source-only facts appear in versioned custom.data only when mapped there. See the Output example section for the exact dataset fields and diagnostic rows.

The Actor reads LinkedIn's job-criteria list. Mapped jobFunction, industries and applicantsCount values appear in custom.data; employment type and seniority are not separate output columns.

Mapped hiringContact* facts from the "Meet the hiring team" block appear in custom.data when LinkedIn names a poster; otherwise they are absent rather than guessed.

LinkedIn's guest cards never carry salary data, so no salary field is emitted — we don't fabricate one.

A run that returns zero postings (or hits an unexpected error) still finishes successfully and pushes a single non-billed diagnostic row with a warnings field explaining why — so an empty result is never a silent failure.

How to scrape LinkedIn jobs with this Actor

1. Click Try for free / Run — no login to the target site, no cookies, no proxies to configure.
2. Adjust the input (keyword, filters, maxItems) or keep the defaults.
3. Run it and export the dataset as JSON, CSV or Excel, or read it over the API.

Run it from your own code:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("nomad-agent/linkedin-scraper").call(run_input={"maxItems": 50})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["title"], "—", item["company"], item["url"])

Or a single HTTP call that runs the Actor and returns items in one response:

curl -X POST \
  "https://api.apify.com/v2/acts/nomad-agent~linkedin-scraper/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"maxItems": 50}'

Input

Field	Type	Default	Notes
keyword	string	""	Job title, skill or role to search for (e.g. "software engineer", "product manager react"). Leave empty to…
location	string	""	City, region or country to filter by (e.g. "Spain", "London", "European Union"). Leave empty for worldwide…
remote	boolean	false	When enabled, restricts results to remote-eligible postings.
postedWithin	string	24h	Keep only jobs published inside this window. Use any for no date filter, or a duration — 1h, 24h, 3d, 2w, 6m. Jobs the source published no date for are kept rather than dropped. Sent to LinkedIn as its own f_TPR window — the narrowest rung that still contains your window — then cut exactly client-side. Replaces timeFilter, postedSince, still accepted for existing integrations.
maxItems	integer	100	Maximum number of job postings to return. Hard ceiling: ~200 per run — LinkedIn's guest search endpoint stops paginating past that offset no matter what you set here (0 = "no limit" still caps at ~200).
includeDescription	boolean	true	Fetch and include the full plain-text job description for each posting. Disabling this makes runs faster…
includeCompanyInfo	boolean	false	Also read structured job details (employmentType, seniorityLevel, jobFunction, industries, applicantsCount) from each posting. Reuses the description request — no extra requests when includeDescription is already on.
includeHiringContact	boolean	false	Also read the job poster (hiringContactName, hiringContactTitle, hiringContactUrl) from each posting — who to contact about the role. Reuses the description request — no extra requests when includeDescription is already on. Present on ~25% of postings.
skipJobId	array of strings	[]	Explicit dedup list — drop any posting whose numeric job id is in this array. Use it to skip ids you already have, in a single call.
titleExclude	array of strings	[]	Drop a result if its title contains any of these words or phrases (case-insensitive).
companyExclude	array of strings	[]	Drop a result if its company name contains any of these words or phrases (case-insensitive).
cacheTtlSeconds	integer	1800	Reuses the last fetch for this many seconds so rapid re-runs don't hit LinkedIn again. Set 0 to always fetch live.

Every filter and the explicit skipJobId dedup list default to off/empty, so existing integrations see no behavior change unless you opt in.

Output example

Every row follows nomad-agent-job-row-v1, the one shape shared by all of this fleet's job Actors. A row carries every field; null means the source did not publish that fact, and locations: [] means no usable location was parsed from the posting. Nothing is guessed.

{
  "schemaVersion": "nomad-agent-job-row-v1",
  "recordType": "posting",
  "source": "linkedin",
  "id": "a1b2c3",
  "url": "https://example.com/linkedin/jobs/a1b2c3",
  "title": "Senior Backend Engineer",
  "company": "Example Company",
  "locations": [
    "Bilbao",
    "Spain"
  ],
  "postedAt": "2026-09-02T00:00:00Z",
  "deadline": null,
  "description": "The complete posting body as plain text, exactly as the source published it — never truncated.",
  "descriptionHtml": "<p>The complete posting body as the source's own markup.</p>",
  "workType": null,
  "custom": {
    "schemaId": "nomad-agent-job-custom-linkedin-v1",
    "data": {
      "jobFunction": "…",
      "industries": "…",
      "applicantsCount": "…"
    }
  }
}

Field	Meaning
schemaVersion	Always "nomad-agent-job-row-v1".
recordType	"posting" for a job, "diagnostic" for a row reporting something about the run itself.
source	Which job source the posting came from, from the collector registry's vocabulary — not the Actor name.
id	Stable identifier for the posting within source.
url	Canonical public URL of the posting on the source site.
title	Job title exactly as the source publishes it, untruncated.
company	Employer name as published.
locations	Places the role is based, most specific first — e.g. ["Bilbao", "Spain"].
postedAt	When the source published the posting, ISO-8601 UTC (YYYY-MM-DDTHH:MM:SSZ).
deadline	Closing date for applications as an ISO-8601 calendar date (YYYY-MM-DD).
description	The complete posting body as plain text — never truncated, never summarised.
descriptionHtml	The posting body as the source's own markup, preserving lists, headings and links.
workType	Working arrangement: "remote", "hybrid" or "onsite".
custom	Facts only this source publishes, as {"schemaId", "data"}.

A run also emits diagnostic rows — recordType: "diagnostic" with a warnings array — when it has something to report, such as a source returning nothing. They are never billed and are easy to filter out on recordType.

Pricing

$0.0009 per job returned — and nothing else. No start fee, no subscription, no rental. 100 jobs ≈ $0.09. Every run returns and bills its current matching snapshot.

Integrations

Export results as JSON, CSV or Excel; connect via Make, Zapier or n8n; call directly with run-sync-get-dataset-items; or plug into AI agents through the Apify MCP server.

Use cases

• Job-alert bots and job boards that need fresh LinkedIn postings
• Recruiting and sourcing pipelines tracking who is hiring
• Salary and hiring-market research by role or region
• AI agents that match candidates to live openings

FAQ

Is it legal to scrape LinkedIn jobs? This Actor reads only publicly available job postings — data any visitor can see without logging in. No personal data behind authentication is touched. Review the target site's terms and your local regulations for your specific use case.

Do I need an account on the target site? No. Postings are fetched from public pages/APIs — no login, cookies or session tokens.

How fresh is the data? Every run fetches live listings. Results are cached for cacheTtlSeconds (default 30 min, set 0 to always hit the source live).

How many jobs can I get? maxItems caps the run (set 0 where supported for no cap). Most sources paginate from newest to oldest.

Does it work without a LinkedIn account? Yes. The scraper reads LinkedIn's public guest job-search endpoint, so no login, cookies or session tokens are needed.

Something broken or missing? Open an issue on the Actor's Issues tab — it is monitored and reliability fixes ship fast.

Is this Actor useful to you? A quick ⭐ review on the Actor's Reviews tab helps other job-alert and recruiting users find it — and tells us what to build next.

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