LinkedIn Jobs Scraper

A reliable LinkedIn jobs data API for production applications.

Query LinkedIn's public job listings and get back typed, structured records — job titles, companies, locations, salaries, full descriptions, applicant counts, and the person who posted the job where LinkedIn names them.

Built to be scheduled and depended on, not run once by hand:

• Typed output. applicants is a number, posted_at is an ISO 8601 datetime, industries is a list. Nothing downstream has to parse strings.
• Stable field names. Documented in the output schema and versioned, so the table you load into does not break under you.
• Honest nulls. has_details tells you whether a posting's page was actually read, so a blank salary means "this job does not state one" rather than "we never looked".
• Safe to re-run. A run that gets moved between machines mid-way resumes where it left off and never writes the same posting twice.
• Incremental by design. skipJobIds drops postings you already have before they cost anything, so polling on a schedule stays cheap.

No login. No cookies. No LinkedIn account of yours is involved at any point. Everything returned is what LinkedIn shows a logged-out visitor.

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What you can do with it

• Job boards and aggregators — keep a feed of fresh listings for a role, a city, or a set of companies.
• Recruitment and talent research — watch who your competitors are hiring and what they are paying.
• Lead generation — a company hiring three sales engineers is a company with a budget. The poster's name and profile come with the listing, where LinkedIn shows one.
• Market research — salary bands, remote-work adoption, and which skills keep appearing, over time.

Quick start

Paste a LinkedIn search URL, or fill in the fields:

{
  "keywords": "python developer",
  "location": "Berlin",
  "maxItems": 100,
  "fetchDetails": true
}

That returns 100 job postings with their full descriptions. To also get who posted each one:

{
  "keywords": "recruiter",
  "location": "United States",
  "maxItems": 100,
  "fetchDetails": true,
  "withRecruiter": true
}

Input

Everything is optional, but a run needs something to search for: either jobUrls, or at least one of keywords, location, geoId, companyIds — or a searchUrl that carries them. A bad input fails immediately and tells you why, before any work is done.

What to search for

Field	Type	Description
searchUrl	string	Paste a LinkedIn jobs search URL and its filters are read out of it. Anything you also set below wins.
keywords	string	Free text, as you would type it into LinkedIn's search box.
location	string	A place name, e.g. Berlin or United Kingdom. Resolved to LinkedIn's own place id when the name is unambiguous.
geoId	string	LinkedIn's numeric id for a place. Use it when you already know exactly which "London" you mean.
distance	integer	Radius in miles around the location, 0–100. LinkedIn ignores it for remote searches.
experienceLevels	array	internship, entry_level, associate, mid_senior, director, executive
jobTypes	array	full_time, part_time, contract, temporary, volunteer, internship, other
workplaceTypes	array	on_site, remote, hybrid
datePosted	string	any_time, past_month, past_week, past_24_hours
salary	string	Minimum USD band, 40k … 200k. LinkedIn applies this to US searches only.
sortBy	string	relevance (LinkedIn's default) or date — use date when polling for what is new.
easyApply	boolean	Only postings applied to on LinkedIn itself.
companyIds	array	Numeric LinkedIn company ids, to restrict the search to those employers.

Or: specific postings

Field	Type	Description
jobUrls	array	Fetch these exact postings instead of searching. A posting URL, a share link, or the bare numeric id all work. Every search field above is ignored.

How much, and how detailed

Field	Type	Default	Description
maxItems	integer	100	How many postings to return, up to 1000.
fetchDetails	boolean	true	Open each posting for its description, seniority, employment type, job function, industries and applicant count.
withRecruiter	boolean	false	Also return who posted the job. Needs fetchDetails.
includeDescriptionHtml	boolean	false	Keep the description's original formatting alongside the plain text.

Narrowing the results

Applied before each posting is opened, so anything you discard costs nothing.

Field	Type	Description
skipJobIds	array	Postings you already have. Dropped before they are fetched — this is what makes a repeated run cheap.
titleInclude	array	Keep only titles containing at least one of these (case insensitive).
titleExclude	array	Drop titles containing any of these. Applied after the include list.

Performance

Field	Type	Default	Description
concurrency	integer	5	How many postings to fetch at once.
maxRetries	integer	2	Retries when a request comes back blocked or rate limited.
requestTimeoutSecs	integer	120	Searches read LinkedIn live, so this is generous by default.

Output

One row per posting:

{
  "source": "linkedin",
  "job_id": "4417748171",
  "job_url": "https://www.linkedin.com/jobs/view/software-engineer-python-at-acme-4417748171",
  "title": "Software Engineer, Python",
  "company": "Acme Inc.",
  "company_url": "https://www.linkedin.com/company/acme",
  "location": "Berlin, Berlin, Germany",
  "workplace_type": "Remote",
  "posted_at": "2026-07-16T00:00:00Z",
  "posted_text": "1 month ago",
  "salary": null,
  "seniority": "Mid-Senior level",
  "employment_type": "Full-time",
  "job_function": "Engineering",
  "industries": ["IT Services and IT Consulting"],
  "applicants": 171,
  "benefits": "Be an early applicant",
  "description": "About the role…",
  "logo_url": "https://media.licdn.com/…",
  "recruiter_name": "Jane Doe",
  "recruiter_url": "https://www.linkedin.com/in/jane-doe",
  "recruiter_headline": "Talent Acquisition Leader @ Acme",
  "scraped_at": "2026-08-31T10:20:20.838463Z",
  "has_details": true
}

Fields

Field	Notes
job_id, job_url	LinkedIn's own id, and the posting's public URL
title, company, company_url, location	From the search result
workplace_type	Remote, On-site or Hybrid, when stated
posted_at	A real timestamp, not a string you have to parse
posted_text	What LinkedIn displayed, e.g. 3 days ago
logo_url, benefits	The company logo, and lines like "Be among the first 25 applicants"
salary	As written in the posting. Most postings do not state one.
seniority, employment_type, job_function	With fetchDetails
industries	A list, not a comma-joined string
applicants	A number, not "64 applicants"
description	Plain text. Add includeDescriptionHtml for the markup too.
recruiter_name, recruiter_url, recruiter_headline	With withRecruiter
has_details	Whether the posting itself was opened — see below
scraped_at	When this row was read

Types are real types. applicants is 171, not "171 applicants". posted_at is an ISO 8601 timestamp. industries is a list. You should not have to write a parser for a scraper's output.

A field LinkedIn did not state comes back null — never an empty string, never a guess. has_details tells you whether the posting was actually opened, so you can tell "this job has no salary" apart from "this row was never enriched".

Things worth knowing

About 1,000 results per search. LinkedIn's public job search stops producing new listings somewhere past a thousand, whatever is asked for. For more than that, split the search — by location, by datePosted, or by company — and run it more often.

Roughly half of postings name a poster. withRecruiter returns the person LinkedIn shows under "Direct message the job poster". Many postings do not show one, and for those the three recruiter fields are null. That means "this posting names nobody", not "we failed to look".

easy_apply and apply_url are null. LinkedIn stopped showing logged-out visitors whether a posting is Easy Apply or where an off-site application goes. Rather than guess, these come back empty. The easyApply filter still works — you can restrict a search to Easy Apply postings, you just are not told which ones they are afterwards.

The same search twice returns a slightly different slice. LinkedIn rotates results for logged-out visitors. Runs are de-duplicated internally by job id, so you never get the same posting twice in one dataset, but two runs minutes apart will not match exactly.

Public data only. What a logged-out visitor can see. Recruiter contact details, applicant lists and anything behind a login are not included.

Location names can be ambiguous. "Turkey" is a town in North Carolina as well as a country. When a name is resolved to a place, the run summary records which place was chosen — check it if results look geographically odd, and set geoId to settle it.

Polling for new jobs

Sort by date, keep the ids you have seen, and skip them next time:

{
  "keywords": "python developer",
  "location": "Berlin",
  "sortBy": "date",
  "datePosted": "past_24_hours",
  "maxItems": 100,
  "skipJobIds": ["4417748171", "4448412664"]
}

Skipped postings are dropped before they are fetched, so a run that finds nothing new costs almost nothing.

Support

Found a bug, or a field you need that is not here? Open an issue on the actor's Issues tab.