Linkedin Jobs Company Scraper

LinkedIn Jobs & Company Scraper ⚡

Searching for relevant LinkedIn job postings and the companies behind them one by one is slow and hard to scale. LinkedIn Jobs & Company Scraper helps you automatically pull job details in bulk from a LinkedIn jobs search URL. It’s a strong fit for a LinkedIn job scraper, a LinkedIn company scraper, or anyone scraping LinkedIn jobs and companies for lead generation. Teams doing outreach, competitive research, and recruiting workflows can use it without manual copy-paste. In one run, you can scrape up to your chosen max_results and start working with structured job records immediately.

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See the Data: Sample Output

Here's a real record from a single run:

{
  "title": "Senior Data Analyst",
  "location": "Dhaka, Bangladesh",
  "company_name": "Acme Analytics",
  "posted_time_ago": "3 days ago",
  "posted_datetime": "2026-06-04T09:12:30.123456",
  "applicants": "123 applicants",
  "base_salary": "$80,000 - $100,000 a year",
  "additional_compensation": "Bonus eligible",
  "seniority_level": "mid_senior",
  "employment_type": "full-time",
  "job_function": "data_analytics",
  "industries": "information_technology",
  "description_text": "Join our team to build dashboards and work with stakeholders...\n\nRequirements...",
  "description_html": "<div class=\"show-more-less-html__markup\">Join our team...</div>",
  "job_urn": "urn:li:jobPosting:1234567890",
  "job_url": "https://www.linkedin.com/jobs/view/1234567890",
  "apply_type": "External",
  "apply_url": "https://www.linkedin.com/jobs/view/1234567890",
  "recruiter_name": "Jane Recruiter",
  "recruiter_detail": "Talent Partner",
  "recruiter_image": "https://example.com/recruiter.jpg",
  "recruiter_profile": "https://www.linkedin.com/in/jane-recruiter/",
  "company_profile": "https://www.linkedin.com/company/acme-analytics/",
  "company_logo": "https://example.com/company-logo.png",
  "company_id": "9876543210"
}

Output Fields

Field	Type	What It Tells You
title	string	The job title you can match to your target roles.
location	string	Where the role is based to filter by region.
company_name	string	The company offering the job so you can build company lists.
posted_time_ago	string	How recently the job appears (useful for freshness checks).
posted_datetime	string	A computed datetime you can sort and track over time.
applicants	string	Applicant volume text for rough market signal and prioritization.
base_salary	string	Any salary information surfaced in the listing (when available).
additional_compensation	string	Extra compensation or incentives when present.
seniority_level	string	Seniority extracted from job criteria for better filtering.
employment_type	string	Employment type (for example, full-time/part-time) if provided.
job_function	string	The job function category from the listing criteria.
industries	string	Industry tag extracted from job criteria for segmentation.
description_text	string	Plain-text job description for analysis and keyword matching.
description_html	string	The HTML version of the description if you need richer formatting.
job_urn	string	A stable identifier for the job posting in the dataset.
job_url	string	Direct link back to the job posting for validation.
apply_type	string	Whether the job uses “External” or “Easy Apply” flow.
apply_url	string	The apply link captured alongside job details.
recruiter_name	string	Recruiter or job poster name when available on the page.
recruiter_profile	string	Link to the recruiter profile area when captured.
company_profile	string	Link to the company page when available.
company_logo	string	Company logo URL/asset when captured.
company_id	string	Company identifier extracted from the job page details.
error_message	string	Not included in the pushed dataset objects; if a job fetch fails, the record is skipped and you simply get fewer results.

Export your full dataset as JSON, CSV, or Excel from the Apify dashboard.

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Setting It Up

Drop this into your input.json and you're ready to go:

{
  "search_url": "https://www.linkedin.com/jobs/jobs-in-bangladesh",
  "max_results": 50
}

Parameter	Required	What It Does
search_url	✅	The LinkedIn jobs search URL that the actor uses as the starting point for scraping LinkedIn job postings.
max_results	✅	Sets the maximum number of jobs to scrape in this run (must be at least 1).

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What It Does

LinkedIn Jobs & Company Scraper scrapes job postings and fetches detailed job information from the job detail pages, then writes structured job records to your Apify dataset as each job is processed.

Scrape LinkedIn jobs and company details in one pass

The actor collects job-level fields like title, location, company_name, description content, and application info, so you can build both job and company datasets from the same run. This makes it useful for LinkedIn jobs data scraping and “scrape LinkedIn jobs and companies” workflows.

Extract structured job criteria for filtering

It returns job criteria such as seniority_level, employment_type, job_function, and industries when those criteria are present on the listing. That means your downstream analysis doesn’t start from unstructured text alone.

Works best as a LinkedIn job scraper for bulk research

By using your provided search_url and max_results, you can generate a larger list quickly without manually opening each posting. This is especially helpful for LinkedIn job vacancies scraper use cases and LinkedIn lead generation scraper pipelines.

Clean output ready for analysis

Job details include both description_text and description_html, plus direct job_url and apply_url. This gives analysts the flexibility to run text analytics or keep links for review.

Includes resilience when pages fail to load

The actor uses retry logic and will stop pagination when it can’t fetch further results. If individual job details can’t be fetched, those jobs are skipped rather than crashing the entire run.

Overall, LinkedIn Jobs & Company Scraper turns a single jobs search page into a structured dataset you can use immediately—perfect for LinkedIn jobs and company scraper tooling.

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Why LinkedIn Jobs & Company Scraper?

There are plenty of ways to pull data from LinkedIn job pages—here's why LinkedIn Jobs & Company Scraper stands out.

Job details returned as consistent JSON objects

Each scraped job is pushed to your dataset with a consistent set of fields like title, company_name, job_url, and recruiter/company fields when available. That makes it easier to plug into reporting, enrichment, or automation without manual cleanup.

Straightforward run control with max_results

You can cap the run with max_results, which helps you run tests, gather enough data for an experiment, and then scale predictably. This is a common requirement for LinkedIn jobs scraping software workflows that need repeatable output volume.

Built for reliability with retries and fallbacks

The actor implements retries and handles blocked or failed requests so your run can continue when possible. It also attempts multiple ways to populate recruiter and salary-related fields when they appear in different parts of the page.

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Real-World Use Cases

Here's how different teams put LinkedIn Jobs & Company Scraper to work:

Recruiting Teams
A recruiting coordinator needs a fresh list of roles matching specific seniority and employment type preferences. They run the actor on a relevant jobs search URL, then quickly filter the dataset by extracted seniority_level and employment_type to focus on the most promising postings.

Sales and BD Teams
A sales team building a pipeline wants to know which companies are actively hiring. They use LinkedIn Jobs & Company Scraper to scrape LinkedIn job postings with company_name, company_profile, and job URLs, then prioritize accounts based on recency (posted_time_ago) and role fit (job_function).

Marketing Agencies
An agency running competitive messaging research needs to understand what roles companies are advertising. They pull job descriptions (description_text) at scale, then extract themes that guide campaign angles and target-vertical positioning.

Freelance Researchers & Data Analysts
A researcher is comparing hiring trends across locations and categories. They scrape a defined number of results (max_results) and use posted_datetime for time-based analysis while keeping direct job_url links for validation.

Automation Specialists / Developers
A developer wants a repeatable job-data ingestion step for an internal dashboard. They trigger LinkedIn job search scraping automation with the Apify Actor, ingest the dataset objects into their system, and schedule periodic runs to keep the dataset up to date.

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How to Run It

No code required. Here's how to get your first results in under 5 minutes:

1. Open the actor on Apify — go to the Apify actor page at console.apify.com.
2. Enter your inputs — set search_url to your LinkedIn jobs search URL and choose max_results.
3. Configure proxy settings (optional) — if your environment needs it, enable proxy support in the run configuration.
4. Start the run — open the live logs to confirm the actor is processing jobs.
5. Open the Dataset tab — you’ll see job records pushed into the dataset as they’re fetched.
6. Export your results — download JSON, CSV, or Excel from the dataset view.
7. Refine and re-run — adjust search_url and max_results to match your target region or research scope.

The whole setup takes under 5 minutes — results start appearing within seconds of launch.

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Export & Integration Options

Once your data is collected, LinkedIn Jobs & Company Scraper fits directly into your existing workflow.

You can download results in your preferred format (JSON, CSV, or Excel) from the Apify dashboard dataset tab. If you want to connect this output to other tools, you can use Apify’s typical automation routes like Zapier/Make-style flows, or access results programmatically via the Apify API.

If you have downstream steps that should run automatically, you can also integrate run completions with webhooks so your pipeline continues as soon as the dataset is ready.

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Pricing

LinkedIn Jobs & Company Scraper runs on Apify, which includes a free tier — no credit card needed to start. The free tier provides $5 platform credits on sign-up, which is typically enough for several real test runs.

After that, runs are billed per Actor compute unit (CU) and you only pay for platform compute. For heavier workloads, use Apify’s subscription plans, and for exact details check the Apify pricing page.

Start free at apify.com — scale up when you need to.

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Reliability & Limitations

What We Handle	How
Rate limits / blocked requests	Retries and resilience handling during fetch operations
Failed search pagination	Stops pagination when it can’t fetch further results
Missing or partial fields	Fields may be null when a specific detail isn’t available on the page
Individual job detail failures	Jobs that fail to fetch are skipped rather than failing the whole run

Limitations: This actor works with publicly accessible pages it can load during scraping. If a page doesn’t provide certain details (like salary or recruiter fields), those fields may be missing or empty. Private or login-gated content isn’t accessible.

For enterprise-scale needs or custom configurations, reach out and we’ll help.

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Frequently Asked Questions

Is there a free plan?

Yes. Apify offers a free tier with credits for testing runs, so you can try LinkedIn Jobs & Company Scraper before scaling up.

Do I need to log in or create an account on LinkedIn?

You only need the search_url and your run configuration inputs. You do not provide LinkedIn credentials as part of this actor’s input schema.

How accurate is the extracted data?

The extracted fields reflect what’s present on the job pages at the time of scraping. When specific details are not shown on a listing, the corresponding fields won’t be available.

How many results can I get per run?

You can control this with max_results, which sets the maximum number of jobs the actor attempts to scrape in one run.

How fresh is the data?

Freshness depends on how recently the jobs search results and job pages show updated “posted” information. The actor captures posted_time_ago and a computed posted_datetime when it can.

Is this legal? Does it comply with GDPR / CCPA?

This actor is designed to scrape publicly available data that is visible without logging in. You are responsible for ensuring your use complies with GDPR, CCPA, platform Terms of Service, and any applicable local regulations.

Can I export to Google Sheets or Excel?

Yes. You can export from the Apify dashboard dataset tab, including JSON, CSV, or Excel. From there, you can import into Google Sheets or any BI tool that accepts those formats.

Can I schedule this to run automatically?

Yes—Apify supports scheduled runs as part of its platform capabilities. You can set the actor to run automatically and refresh your dataset on a cadence.

Can I access results via the API?

Yes. Apify provides API access for actor runs and dataset retrieval, so you can pull results programmatically and automate downstream processing.

What happens when the actor encounters an error?

If the actor can’t fetch search results, it stops pagination. If fetching job details fails for a specific job, that job is skipped so the run can continue where possible.

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Get Help & Use Responsibly

Got a question about LinkedIn Jobs & Company Scraper or a feature you'd like added? Reach out at dataforleads@gmail.com. We’re happy to help with practical guidance and we actively maintain this actor based on feedback—ideas like additional filtering options or more robust field normalization are welcome.

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publicly available data only. The actor does not access private accounts, login-gated pages, or password-protected content. You’re responsible for complying with GDPR, CCPA, and the platform’s Terms of Service when using the data. For data-removal requests, contact dataforleads@gmail.com. Use responsibly, ethically, and only for lawful purposes.