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💼 LinkedIn Jobs Scraper - FREE TO USE

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Scrape LinkedIn job postings in bulk title, company, exact posting date, location, salary range, seniority, employment type, industry, applicant count, full description and the named recruiter who posted the job. Search by URL or by keywords and locations. No login or cookies needed.

Pricing

Pay per usage

Rating

0.0

(0)

Developer

FlowExtract API

Actor stats

Bookmarked

Total users

Monthly active users

3 days ago

Last modified

Changelog

All notable changes to the LinkedIn Jobs Scraper.

[0.1.0] — 2026-09-13

First release.

Added

Three ways to search — paste a LinkedIn job search URL, paste a single job link, or describe the search with keywords and locations. All three can be combined in one run; results are merged and de-duplicated, and every row records each search that found it.
Plain-language locations and companies — type "London" or "Stripe" and the Actor resolves it on LinkedIn itself. No numeric IDs to look up.
Custom freshness windows — postedWithinHours accepts any window, beyond the 24 hours / 1 week / 1 month that LinkedIn's own filter offers.
Salary parsing — the published range is captured and split into salary_min, salary_max, salary_currency and salary_period, so hourly and annual pay are never averaged together.
Recruiter contacts — poster_name, poster_title and poster_profile_url for postings that name the person who posted them.
Exact posting dates — posted_date is the posting's own date, not a value reconstructed from text like "3 weeks ago".
Applicant counts — with applicants_is_estimate flagging the cases where LinkedIn gave a range rather than a number.
Filters that actually filter — employment type, experience level, workplace type, title and description keywords, company and location exclusions, "has a salary", "has a named recruiter" and a hard date cutoff, all applied to the collected data. When every job is filtered out, the run names the filter responsible.
Card-only mode — scrapeJobDetails: false returns the search-card fields roughly 10× faster, for monitoring which jobs exist.
Five dataset views — Overview, Recruiter contacts, Salary data, Full descriptions and Problems.
Failures are delivered as rows with an error_type rather than being silently dropped, and RUN_SUMMARY records what each search collected, delivered and filtered.

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