LinkedIn Jobs Scraper | $0.9/1K | No Login (Real-Time)
Pricing
from $0.90 / 1,000 job extracteds
Collect public LinkedIn job listings and organize available salary, work-mode, seniority, skills, benefits, freshness, application, and company hiring signals. Filter by role, location, experience, type, age, or remote preference. JSON and CSV export. $0.9/1K successful jobs.
Choose how jobs are discovered. Auto-detect preserves existing integrations: pasted LinkedIn search URLs take precedence, otherwise keywords are used.
Job title or keywords. Examples: 'software engineer', 'marketing manager'. Used only when 'urls' is empty.
Optional. One or more LinkedIn jobs search URLs (copy from the address bar after applying filters on linkedin.com/jobs/search). When provided, they replace the keyword search above; filters from the URL itself are used. Results from multiple URLs are merged and deduplicated.
City, state, country, or 'Remote'. Examples: 'New York', 'United States', 'Remote'.
Maximum number of result pages to process. The number of jobs returned per page varies by query, filters, location, and current availability.
Stop after this many delivered jobs. This is the main cost and output-size guard for MCP and automation runs.
Prefer remote jobs. Since LinkedIn's 2026 AI search removed the old remote URL filter, this is merged into the search keywords as 'remote'.
Filter by required experience. Since LinkedIn's 2026 AI search removed the old experience URL filter, the selection is appended to the search keywords (e.g. 'mid-senior level').
Filter by employment type. Since LinkedIn's 2026 AI search removed the old job-type URL filter, the selection is appended to the search keywords (e.g. 'contract').
Filter by when the job was posted
Only jobs with LinkedIn's Easy Apply flow (f_AL=true). Works with both keyword mode and pasted search URLs.
Only jobs with fewer than 10 applicants (f_EA=true). Works with both keyword mode and pasted search URLs.
Extract job description preview
Extract salary as shown on LinkedIn (free text). The parser turns this into salaryMinUsd / salaryMaxUsd / salaryMedianUsd.
Parse salary text into salaryMinUsd, salaryMaxUsd, salaryMedianUsd, salaryPeriod, salaryCurrency. Annualizes hourly/daily/weekly/monthly to year. Supports USD/EUR/GBP/CAD/AUD/INR/JPY.
Convert 'Posted X days ago' into daysSincePosted + freshness_tier (today / this_week / this_month / older).
Classify as remote / hybrid / onsite based on title + location + description signals.
Extract skillsRequired[] (200+ tech terms), softSkills[], certifications[] from job title + description.
14 benefit boolean flags: mentions_401k, mentions_health_insurance, mentions_equity, mentions_remote_work, mentions_visa_sponsorship, mentions_relocation, mentions_unlimited_pto, mentions_parental_leave, mentions_signing_bonus, mentions_4_day_week, mentions_stipend, mentions_meals, mentions_gym, mentions_commuter_benefits.
Normalize the title into one of: intern / junior / mid / senior / lead / staff / principal / director / vp / c-level / unspecified.
Auto-classify as engineering, data_science, product, design, sales, marketing, finance, hr, operations, legal, customer_support, healthcare, education, construction_trades, or other.
Add a 0-100 company hiring-market research signal with a tier and high-level reasons. This is not a candidate score and must not be used for automated employment decisions.
Add company and role research links plus draft outreach ideas. Review every destination and message before use, respect opt-outs, and follow applicable privacy and anti-spam rules.
Identify selected DEI-related phrases mentioned in listing text. These keyword indicators do not establish a company's actual policies, culture, or legal compliance.
Add location-based pay-transparency research indicators and note whether salary text is present. These heuristic fields do not determine legal coverage or compliance; verify the listing and consult qualified counsel.
Parse San Francisco, CA → city, state, country: US, isUsListing, isRemoteListing. Used by pay-transparency detection and CRM filtering.
Request additional available details for jobs from the top N observed companies. Set to 0 to disable. This option increases processing time and fields remain availability-dependent.
Request additional available details for every collected job, including fuller descriptions, salary, seniority, application method, applicant count, and industries. Disable for a lighter discovery run.
Fetch each unique company's LinkedIn page (up to 25 companies per run) and add companyWebsite, companyEmployeesCount, companyDescription, and companyIndustry when available. Skipped automatically when the run's time budget is nearly exhausted.
If enabled, only the first job from each unique company is kept.
Drop jobs whose recruiterScore is below this value (0-100). Useful for high-intent prospect lists. 0 = no filter.
Drop jobs where salary couldn't be parsed (no comp transparency).
Drop hybrid / onsite / unknown work-mode jobs.
default = full JSON (all 30+ fields). csv = flattened sales-ready 25-column shape for HubSpot / Pipedrive / Salesforce import.
Compact returns core job facts and links. Recruiting adds salary, skills, application and hiring signals. Full keeps every available field. Existing API calls that omit this field keep the legacy full output.
On runs with multiple jobs, write three free aggregate records to the run's KV store: SUMMARY (avg salary, top skills, recruiter-tier distribution), TOP_HIRING_COMPANIES (top 20 companies sorted by job count with 3 outreach pitch variants), TOP_JOBS (top 20 jobs sorted by recruiterScore — sales-ops job-level digest).
How many top companies to include in the TOP_HIRING_COMPANIES KV record.
How many top jobs (sorted by recruiterScore) to include in the TOP_JOBS KV record.
Retry attempts when a page is temporarily unavailable or returns no usable rows.
Maximum number of pages processed in parallel. The default is recommended for most runs.
