LinkedIn Jobs Scraper & Hiring Signal Intelligence

Scrape public LinkedIn job listings without login or cookies and turn job postings into structured hiring intelligence.

This LinkedIn Jobs Scraper finds public jobs, enriches each result from its logged-out job page, and calculates an explainable Hiring Signal Score from evidence observed during the current run. It is a focused LinkedIn Job Scraper and an API-ready source of Job Listings Data, Recruiting Data, and Labor Market Intelligence—without a LinkedIn account, user cookies, paid data API, browser, or residential proxy.

What it does

Enter job keywords and a location. The Actor searches LinkedIn's public logged-out jobs experience, deduplicates postings by public job ID or canonical URL, fetches public job details with moderate concurrency, and writes one real observed job per Dataset row. It also aggregates the delivered evidence by company, role, and location.

The result can be consumed through the Apify API as a practical LinkedIn Jobs API workflow. It is not an official LinkedIn API.

Why it is different

Most job scrapers stop at a list of postings. This Actor adds deterministic Hiring Intelligence:

• unique jobs observed per company in this run;
• distinct observed role titles and locations;
• deterministic role clusters;
• an explainable 0–100 Hiring Signal Score;
• HIGH, MEDIUM, or LOW observed-activity levels;
• confidence and plain-English evidence for every score;
• a responsive executive REPORT.html.

The score never claims that a company is growing, expanding, or urgently hiring. It measures only the public postings observed inside one run.

Key features

• Public LinkedIn jobs only; no login, cookies, credentials, or authenticated session
• HTTP-only architecture with no Playwright or Chromium
• Public guest search endpoint with the logged-out public search page as fallback
• Public job-detail enrichment with isolated per-job failures
• Date, workplace, employment, salary, applicant, seniority, industry, company, and apply fields when explicitly exposed
• Missing information remains null
• Deduplication by job ID, with canonical job URL fallback
• Moderate concurrency, 15-second HTTP timeouts, two limited retries, and short exponential backoff
• Dataset, machine-readable OUTPUT, and executive REPORT.html
• Pay Per Event billing prepared for valid unique delivered jobs only

Input

Field	Type	Default	Description
keywords	string	Software Engineer	Job title, skill, or keyword
location	string	United States	City, region, or country
maxResults	integer	10	Unique jobs to deliver, from 1 to 200
datePosted	select	any	any, past_24_hours, past_week, past_month
workplaceType	select	any	any, remote, hybrid, onsite
employmentType	select	any	any, full_time, part_time, contract, temporary, internship
sortBy	select	relevance	relevance or recent
includeDescription	boolean	true	Include the observed public job description

Example input

{
  "keywords": "Software Engineer",
  "location": "United States",
  "maxResults": 10,
  "datePosted": "any",
  "workplaceType": "any",
  "employmentType": "any",
  "sortBy": "relevance",
  "includeDescription": true
}

Dataset fields

Each Dataset item is one unique public job:

• identity: job_id, title, job_url, source, retrieved_at;
• company: company_name, company_url, company_id, industry;
• job evidence: location, workplace_type, employment_type, seniority, posted_at, posted_days_ago, applicant_count, description, apply_url;
• compensation: salary_min, salary_max, salary_currency, salary_text;
• intelligence: company_jobs_observed, company_distinct_roles_observed, company_distinct_locations_observed, role_cluster, hiring_signal_score, hiring_signal_level, hiring_signal_confidence, hiring_signal_reasons, warnings.

salary_min and salary_max preserve the numeric values and scale displayed publicly. They are not annualized. A bare $ does not establish an ISO currency, so salary_currency remains null unless the currency is explicit or the symbol is unambiguous.

Hiring Signal Score

The company-level score is deterministic and capped at 100:

Component	Maximum	Rule
Observed hiring volume	45	5 points per unique job observed, capped at 45
Recency	25	Most recent observed job: ≤1 day 25; ≤3 days 20; ≤7 days 15; ≤30 days 8; older 2; unavailable 0
Role breadth	15	3 points per distinct observed title, capped at 15
Geographic breadth	10	0 locations 0; 1 location 2; 2 locations 5; 3 locations 7; 4+ locations 10
Evidence completeness	5	Coverage of public fields across the company's observed records

Levels are:

• HIGH: 70–100 — high observed hiring activity in this run
• MEDIUM: 40–69 — moderate observed hiring activity in this run
• LOW: 0–39 — limited observed hiring evidence in this run

Confidence is separate from intensity. It is based on public field completeness and date coverage; it does not predict business outcomes.

Hiring Intelligence

The Actor classifies titles with transparent keyword rules into Engineering, Sales, Marketing, Finance, Operations, Customer Service, Human Resources, Healthcare, Legal, Data, Product, Design, or Other. No external AI model is used.

Company totals, role breadth, location breadth, top companies, top role clusters, top locations, remote share, salary coverage, and date coverage refer only to records observed in the current run. A small maxResults intentionally provides a smaller evidence window.

Use cases

• Recruiting research and sourcing workflows
• Job market research and Labor Market Intelligence
• Competitive hiring intelligence based on observed public postings
• Sales intelligence and Lead Generation Hiring Signals
• Workforce analysis and location-demand snapshots
• Automation with Apify schedules, webhooks, datasets, and integrations
• AI agents and RAG datasets that need structured job evidence

Output

• Dataset — one valid unique job per row, exportable as JSON, CSV, Excel, XML, RSS, or through the Apify API.
• OUTPUT — search, filters, status, counts, coverage, rankings, high-signal companies, warnings, methodology, source attempts, and performance.
• REPORT.html — search summary, metrics, HIGH/MEDIUM/LOW company counts, top hiring companies, roles, locations, remote share, salary coverage, jobs, score evidence, links, warnings, and methodology.

An empty or blocked search produces a successful empty output with warnings and no billable job events. A failed detail page retains a valid search-card job when its identity, title, company, and public URL remain observable.

Data integrity

The Actor does not infer missing salaries, currency, applicant counts, workplace type, employment type, seniority, industry, location, or posting date. Unobserved optional values are null. Relative public date labels can produce posted_days_ago; an exact posted_at is emitted only when an exact public date/time attribute is present.

Applicant phrases such as “Be among the first 25 applicants” or “Over 200 applicants” are not treated as exact applicant counts. A public LinkedIn job URL is retained as evidence for every delivered row.

Limitations

• LinkedIn can change, restrict, rate-limit, localize, or remove its logged-out pages and HTML without notice.
• Results depend on the network region, query, public inventory, ordering, and selected result cap.
• Public search filters and returned fields may vary by country and job.
• A posting can expire between search and detail retrieval.
• Relative dates use deterministic day equivalents (hours = 0, weeks = 7 days each, months = 30 days each); they do not create a fabricated exact timestamp.
• Applicant counts, salary, workplace type, industry, and apply links are often absent.
• This is a point-in-time observation, not a complete census or business forecast.

Pricing

The prepared Pay Per Event event is job-result at USD 0.0005 per valid unique job delivered—USD 0.50 per 1,000 results. Empty searches, duplicates, inaccessible pages, discarded rows, errors, warnings, OUTPUT, and REPORT.html are not billed.

The source package does not activate monetization. During publishing, configure only the custom job-result event, disable automatic default-Dataset-item charging to prevent double billing, remove the synthetic start event for a no-start-fee launch, and leave “Pay per event + usage” off.

Privacy and responsible use

This Actor accesses only public logged-out job pages. It does not access private profiles, collect private emails or phone numbers, use authenticated sessions, solve access challenges, or attempt aggressive access-control evasion. Use public data in accordance with applicable laws, platform terms, privacy obligations, and your legitimate purpose. Do not use results for unlawful discrimination or solely automated high-impact employment decisions.

Independent product disclaimer

This Actor is independent and is not affiliated with, endorsed by, sponsored by, or officially connected to LinkedIn or Microsoft.