LinkedIn Jobs Scraper - No Login Required

Fast, reliable LinkedIn job scraper that extracts 20+ structured fields from public job listings. No LinkedIn account, cookies, or API key needed.

Why This Actor?

• 20+ fields included — seniority level, industries, job function, skills, and more. Other actors charge $30/month extra for these fields.
• No login, no cookies — scrapes LinkedIn's public job pages. Zero setup, zero risk to your LinkedIn account.
• Skills extraction — automatically extracts skills mentioned in job descriptions. No upsell, no "Advanced" tier.
• Cross-query deduplication — run multiple overlapping queries and get clean, unique results.
• Concurrent fetching — configurable parallelism (1-20 workers). Scrape 1,000 jobs in ~2.5 minutes.
• Reliable — exponential backoff with jitter on rate limits (4 retries). Proxy rotation built in.
• Boolean search — use LinkedIn's native search operators: "data engineer" AND (Python OR SQL) NOT junior.
• Lightweight — runs on 256MB memory, keeping your compute costs low.

Cost

$0.50 per 1,000 results (platform usage included).

Results	Cost
100	$0.05
1,000	$0.50
10,000	$5.00

Input

Field	Type	Default	Description
queries	string[]	["software engineer"]	Search queries (supports boolean operators)
location	string	""	Location name (e.g. "Finland", "New York")
geo_id	string	""	LinkedIn GeoID — overrides location if set
max_results_per_query	integer	25	Max results per query (up to 1000)
date_posted	enum	""	Filter: "", pastDay, pastWeek, pastMonth
experience_level	string[]	[]	LinkedIn codes: 1=Internship, 2=Entry, 3=Associate, 4=Mid-Senior, 5=Director, 6=Executive
concurrency	integer	5	Parallel detail requests (1-20)
min_delay_ms	integer	300	Minimum delay between requests (ms)
max_delay_ms	integer	800	Maximum delay between requests (ms)
proxy	object	Apify proxy	Proxy configuration
include_description_html	boolean	false	Include raw HTML description in output

Example Input

{
    "queries": [
        "software engineer",
        "\"data scientist\" AND Python NOT junior"
    ],
    "location": "San Francisco",
    "max_results_per_query": 100,
    "date_posted": "pastWeek",
    "experience_level": ["4", "5"],
    "concurrency": 10
}

Output

Every result includes 20+ fields. All fields are present in every result — missing data returns null, never errors.

Field	Type	Description
job_id	string	LinkedIn job ID
job_url	string	Direct URL to the job posting
job_title	string	Job title
company_name	string	Company name
location	string	Job location
job_description	string	Plain text description
seniority_level	string	e.g. "Mid-Senior level", "Director"
employment_type	string	e.g. "Full-time", "Contract"
job_function	string	e.g. "Engineering", "Information Technology"
industries	string	e.g. "Software Development"
skills	string[]	Skills extracted from the job description
salary_range	string	Salary info if listed
posted_date	string	ISO date when posted
applicant_count	string	e.g. "Over 200 applicants"
company_url	string	LinkedIn company page URL
workplace_type	string	"Remote", "On-site", or "Hybrid"
apply_url	string	External application URL if available
description_html	string	Raw HTML description (only if include_description_html is true)

Example Output

{
    "job_id": "3812345678",
    "job_url": "https://www.linkedin.com/jobs/view/3812345678",
    "job_title": "Senior Software Engineer",
    "company_name": "Acme Corp",
    "location": "San Francisco, CA",
    "job_description": "We are looking for a senior software engineer...",
    "seniority_level": "Mid-Senior level",
    "employment_type": "Full-time",
    "job_function": "Engineering",
    "industries": "Software Development",
    "skills": ["Python", "AWS", "Kubernetes", "React", "PostgreSQL"],
    "salary_range": "$150,000 - $200,000",
    "posted_date": "2026-03-15",
    "applicant_count": "Over 200 applicants",
    "company_url": "https://www.linkedin.com/company/acme-corp",
    "workplace_type": "Hybrid",
    "apply_url": "https://acme.com/careers/12345"
}

Usage Tips

• Start small: Test with 1 query and 5-10 results before scaling up
• Use boolean search: Combine terms with AND, OR, NOT and quotes for exact phrases. Example: "product manager" AND (B2B OR SaaS) NOT intern
• Use date filters: pastWeek reduces results and speeds up scraping
• Tune concurrency: Start at 5, increase to 10-15 with proxy enabled
• Enable proxy for large runs: Scraping 500+ jobs without proxy may hit rate limits
• Combine queries: Use multiple specific queries rather than one broad query for better results
• GeoID vs location: GeoID is more precise — use it if you know the LinkedIn code for your target region

Common Use Cases

• Job boards and aggregators — feed fresh listings into your platform
• Recruitment pipelines — monitor openings at target companies
• Salary benchmarking — collect compensation data across roles and regions
• Market research — track hiring trends, skill demand, and competitive intelligence
• Personal job alerts — monitor 100 jobs/day for just $0.05

Limitations

• LinkedIn public pages show up to ~1000 results per search query
• Salary information is only available if the employer has listed it
• Some fields (applicant count, apply URL) may not be present on all listings
• Rate limits apply even with proxy — the actor handles this with retry and backoff

Disclaimer

This actor scrapes publicly accessible LinkedIn job pages that do not require login. It is your responsibility to ensure your use complies with LinkedIn's Terms of Service and all applicable laws. The author provides this tool as-is and assumes no liability for how it is used.