How to Scrape LinkedIn Jobs in 2026 (After the AI Search Change)

Short answer: build a job search on LinkedIn in a private browser window, copy the URL, and paste it into LinkedIn Jobs Scraper, or type keywords and a location straight into its input. You get one row per job with title, company, location, salary where shown, posted date, applicant count, the full description and, optionally, company details. It costs $0.002 per job, so 1,000 postings cost $2, and no LinkedIn login is involved.

Job postings are some of the most useful public data there is. Recruiters use them to see who is competing for talent, sales teams use them as buying signals, job seekers use them to find roles before they fill, and analysts use them to track which skills are in demand. LinkedIn has the biggest share of that data and no export. This guide covers how to get it out, including the change LinkedIn made to job search in August 2026 that broke many old saved searches.

LinkedIn Jobs Scraper is built by curious_coder, not by me. It has over 160,000 users on Apify and is on my list of the best Apify actors for B2B data.

What data do you get for each LinkedIn job?

• Job ID and direct link to the posting
• Job title and location
• Company name, company LinkedIn URL and logo
• Salary information, when the employer publishes it
• Posted date and benefits labels such as "Actively hiring"
• Applicant count, when LinkedIn shows it
• The full description as HTML and as plain text
• The name and title of the person who posted the job, when shown
• Optional company details if you switch on company scraping

The poster's name is often the hiring manager or recruiter, which is why this data is popular with recruiting agencies and with salespeople who sell to hiring teams.

What changed with LinkedIn job search in 2026?

In August 2026 LinkedIn moved job search to an AI-powered search that only supports a few URL filters: date posted, company, Easy Apply and "under 10 applicants". Older filters such as experience level, job type and remote or on-site no longer work as separate URL parameters. LinkedIn now expects you to describe them in the search words, for example "senior remote data engineer" instead of a keyword plus two filters.

The scraper has an option to convert old search URLs into the new AI search format automatically, turning experience level, job type and workplace filters into search terms. Filters with no public label, such as industry or job function IDs, cannot be converted, so describe those in your keywords yourself.

How do you scrape LinkedIn jobs step by step?

1. Decide how to search. Either paste LinkedIn job search URLs, or leave the URL list empty and fill in Keywords, Location, Date posted and optionally Company IDs or Under 10 applicants. If you give URLs, the filter fields are ignored.
2. If using URLs, open LinkedIn Jobs in a private window, run your search, apply the filters and copy the full address.
3. Put the details in the words. Seniority, contract type and remote status go in the keywords now.
4. Choose date posted. For signals and alerts, "past 24 hours" or "past week" keeps the data fresh and the cost low.
5. Switch on company details if you need company size, industry and website for each job.
6. Run and export to Excel, CSV or JSON, or schedule it daily.

How much does it cost?

$0.002 per job returned, plus a negligible run start fee.

• 1,000 postings: $2.00
• A daily search returning 150 new postings, for a month: about $9.00

Apify's free plan includes $5 of monthly credit, which covers about 2,500 postings.

What can you do with LinkedIn job data?

Use hiring as a buying signal

A company posting three sales development roles is scaling outbound and about to buy sales tools. A first security engineer means a new security budget. Search for the roles that signal a need for what you sell, then group postings by company and count them.

Track the job market for a skill

Run the same search weekly and count postings, salary ranges and the tools named in descriptions. The plain-text description field is ideal for this: the Bulk LLM Runner can pull out required skills, seniority and salary into columns across thousands of descriptions in one run.

Catch jobs before they fill

Filter for "under 10 applicants" and "past 24 hours", schedule it hourly or daily, and get alerts for new postings. To receive only the new ones rather than the whole list, chain the Dataset Diff actor after the scraper; it reports only rows that are new since the last run and can post them to Slack, Telegram or email.

Is LinkedIn the only place to get job postings?

No, and for some jobs it is not the best one. Many companies post first, or only, on their own applicant tracking system board: Greenhouse, Lever, Ashby and others. If you already have a list of target companies, it is more complete to read their boards directly. That approach is covered in how to find which companies are hiring from a list of domains, using my Company Domain to Job Postings actor. For the Gulf job market, the Naukrigulf scraper covers the region's main job board.

Is it legal to scrape LinkedIn jobs?

The scraper reads job listings that LinkedIn shows publicly, without logging in, so no LinkedIn account is involved or at risk. Job postings are published to be read, and using them for market research and sales signals is common. Two cautions: the poster's name and title are personal data under GDPR and similar laws, so handle them with care, and LinkedIn's terms restrict automated access, so do not republish postings as your own job board. This article is independent of LinkedIn.

Frequently asked questions

More hiring and B2B data sources are in the lead generation hub.