LinkedIn Jobs Scraper

Hiring-intent and buying-signal data at scale: scrape public LinkedIn jobs for titles, companies, parsed salary, skills, descriptions, and apply links across 20+ countries — no LinkedIn account, no API key.

What you get

LinkedIn hosts 20M+ active job postings across 200+ countries — every one of them is a buying signal. A company hiring a "Salesforce Admin" is a Salesforce customer; a company posting 10 backend roles in 30 days is in growth mode and buying tooling. This scraper returns titles, companies, locations, parsed salary ranges (min/max/currency/period), skills, experience levels, job type, full descriptions, applicant counts, and direct apply links. No LinkedIn account needed, no API key.

LinkedIn Jobs API alternative for hiring intelligence

LinkedIn does not publish a public Jobs API. B2B sellers and RevOps teams use this actor as the structured-data alternative for tracking hiring intent: which companies are hiring for your ICP's tech stack, which competitors are scaling specific functions, and which accounts just opened 5+ new headcount in your target segment. The skills, description, and company_name fields are the load-bearing ones for buying-signal pipelines — they let you match job posts to product categories (e.g. "hiring SDR" → outbound tooling buyer; "hiring Snowflake engineer" → data-stack buyer).

Map company hiring trends from job postings

Filter by companyName to track competitor or target-account hiring velocity over time: how many roles, which functions, what locations, what experience levels. Combine with the LinkedIn Company Employees Scraper to size the team, then use this actor to size the delta — net new headcount as a leading indicator for funding rounds, market expansion, and buying intent. Recruiters use the same data as a sourcing-intelligence layer (where talent is flowing to/from) and investors use it as a portfolio-monitoring signal.

Output fields

Field	Description
title	Job title
company_name	Hiring company
location	Job location
salary_raw	Salary as displayed
salary_min	Parsed minimum salary
salary_max	Parsed maximum salary
salary_currency	Currency code
salary_period	Pay period (yearly, monthly, hourly)
experience_level	Required experience level
job_type	Full-time, Part-time, Contract, etc.
industry	Company industry
skills	Required skills (Standard and Full modes)
description	Full job description (Standard and Full modes)
applicant_count	Number of applicants
is_easy_apply	Whether Easy Apply is available
posted_at	Posting date
apply_url	LinkedIn job URL

Example output

{
    "title": "Software Engineer",
    "company_name": "Google",
    "location": "San Francisco, CA",
    "salary_raw": "$150,000 - $200,000/yr",
    "salary_min": 150000,
    "salary_max": 200000,
    "salary_currency": "USD",
    "salary_period": "yearly",
    "experience_level": "Mid-Senior level",
    "job_type": "Full-time",
    "industry": "Technology, Information and Internet",
    "skills": ["Python", "Java", "AWS"],
    "description": "We are looking for a talented Software Engineer to join...",
    "applicant_count": "200+ applicants",
    "is_easy_apply": true,
    "posted_at": "2026-04-05",
    "apply_url": "https://www.linkedin.com/jobs/view/123456/"
}

Input parameters

Parameter	Required	Description
queries	Yes	Job search keywords (e.g., ["software engineer", "data scientist"]). Each query runs a separate LinkedIn search.
location	No	City or country (e.g., "San Francisco", "London", "Bangalore", "India"). Leave empty for worldwide.
country	No	Country filter: United States, United Kingdom, India, Canada, Australia, Germany, France, Netherlands, Singapore, UAE, Japan, Brazil, Ireland, Sweden, Switzerland, Spain, Italy, Israel, South Korea, Mexico. Leave empty to use the location field instead.
companyName	No	Limit results to a specific company (e.g., "Google", "Netflix").
maxResultsPerQuery	No	Max jobs per query. Default 5 (start small to preview cost; raise for larger runs). LinkedIn shows ~25 per page.
maxPages	No	Number of search result pages per query. Default 1. Each page has ~25 jobs.
scrapeMode	No	standard (default — fastest, gets all fields), full (alternative extraction with fallback), or fast (search cards only, no descriptions).
datePosted	No	Filter: Any time, Past 24 hours, Past week, Past month.
jobType	No	Filter: Full-time, Part-time, Contract, Temporary, Internship.
experienceLevel	No	Filter: Internship, Entry level, Associate, Mid-Senior level, Director, Executive.
proxyConfiguration	No	Proxy settings. Leave default for best results.

Scrape modes

• Standard (recommended): Fastest and most affordable. Gets all fields including descriptions, salary, and skills.
• Full: Alternative extraction method with fallback. Use if Standard returns incomplete data for your queries.
• Fast: Extracts data from search result cards only — title, company, location, posted date, apply URL. Best for bulk collection when descriptions aren't needed.

Use cases

• B2B sales / RevOps (buying signals): Filter for skills: ["Salesforce"] to find Salesforce customers; filter for skills: ["Snowflake"] to find data-stack buyers. Hiring posts are the cleanest public proxy for tech-stack adoption.
• ABM teams: Track hiring velocity at your 200 named accounts — companies posting 10+ roles in 30 days are in expansion mode and primed for outreach.
• Competitive intelligence: Pull every job your top 5 competitors posted last month — see which functions they're scaling, which markets they're entering, and what their salary bands signal about funding.
• Recruiters / talent-sourcing: Power sourcing workflows with fresh public listings, parsed salary, and apply URLs.
• Investors / market analysts: Use net hiring as a portfolio-monitoring leading indicator — growth, contraction, and pivots show up in job posts before press releases.
• Job aggregators: Build global job boards with LinkedIn as the flagship feed.
• Salary analytics: Benchmark parsed salary ranges across roles, levels, and geographies.
• Labor-market research: Study demand by skill, industry, and geography over time.

Use cases & recipes

Step-by-step guides on thirdwatch.dev/blog:

• Build a LinkedIn Jobs Aggregator with Apify (2026 Guide)
• Filter LinkedIn Jobs by Skill and Location (2026 Guide)
• Scrape LinkedIn Jobs Without Login at Scale (2026 Guide)
• Track LinkedIn Hiring Velocity by Company (2026)

-end

Limitations

• Salary fields are populated for roughly 40-60% of listings — LinkedIn shows salary only when the employer opts in.
• skills and description require Standard or Full mode; Fast mode reads only the search cards.
• Easy Apply jobs have an apply_url pointing back to LinkedIn; direct-apply jobs link to the employer's site.
• No login means no personalized recommendations — only public listings.
• Per-query result ceiling: ~1000 jobs. LinkedIn's public guest endpoint stops paginating around the 1000th result. To pull more, split by location, role, job_type, or date_posted and run several queries.
• Match precision is good, not exact. Public guest search is broader than logged-in search — e.g. "account manager" may surface adjacent sales roles. Use specific titles and the job_type / experience_level filters to tighten results. Boolean operators (AND / OR / NOT) are not supported on the public endpoint.
• Very high volumes may hit LinkedIn rate limits; split large pulls across multiple runs.

Compared to alternatives

• vs. LinkedIn's public API: LinkedIn does not offer an open job-search API for third parties. This actor is the structured-data alternative.
• vs. hiring-intent platforms (LinkedIn Talent Insights, Lightcast, Revelio): Those tools cost $20K–$100K/year for enterprise seats. This actor returns the underlying job-post data — title, company, skills, salary, posted_at — at a flat per-result rate, so you can build the same hiring-intent signals in-house.
• vs. curious_coder/linkedin-jobs-scraper ($0.001/result, 45K users): Cheaper per result with a much larger installed base, but returns fewer fields and does not parse salary. Use this actor when you need structured salary, skills, and descriptions with a free trial.
• vs. bebity/linkedin-jobs-scraper (~$0.005/result): Comparable fields, but no tiered volume discounts.

Pairs well with Indeed Scraper and Naukri Scraper for multi-source hiring datasets.

FAQ

Do I need a LinkedIn account? No. The scraper accesses only publicly visible job listing pages.

Why are some fields empty in Fast mode? Fast mode reads only the search cards. Salary, skills, and description require Standard or Full mode.

What if I get blocked? The actor has built-in rate limiting. If you see failures, lower maxPages, shrink queries, or spread runs across the day.

Last verified: 2026-05

More scrapers at thirdwatch.dev.