LinkedIn Company Jobs Scraper — Jobs, Salary & Company Data

Give it a list of companies; get their open LinkedIn jobs as clean rows — title, location, posted date, seniority, employment type, job function, applicant count, the full description, salary and the company's data on every row. No LinkedIn account, no cookies: only LinkedIn's public job pages.

What makes it different, measured on the platform:

• Salary where employers actually state it. LinkedIn's own pay box was filled for only 53 of 897 jobs in our test runs; most employers write the range into the description instead ("The annual US base salary range for this role is $126,600 – $190,000"). The Actor reads both, under strict rules, and tells you where each figure came from (salarySource, salaryRaw). Salary was found for 99 % of US-located jobs (493 of 498; mostly California, New York and Washington, which require pay ranges), 30 % of jobs elsewhere, 68 % overall.
• Worldwide by default. Without a location, LinkedIn quietly searches the United States only (Stripe: 625 US jobs vs 942 worldwide). This Actor always asks for worldwide unless you name a place.
• LinkedIn's own job count on every row (linkedinJobCount) — a hiring-volume signal, and a check that you got everything.
• Seniority and job-type filters that actually filter. LinkedIn ignores these filters on its public pages (we checked: the results do not change). This Actor applies them to what each job page states. About half of the employers we tested state no seniority at all; the run tells you when that is the case.

Quick start (API)

curl -X POST "https://api.apify.com/v2/acts/foxlabs~linkedin-company-jobs-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"companies":["https://www.linkedin.com/company/vercel/","Stripe"],"keywords":"engineer","datePosted":"past-week","maxJobsPerCompany":50}'

What you get

Field	What it is
title, location, postedDate, postedTimeAgo, jobUrl, jobId	From the job listing
seniorityLevel, employmentType, jobFunction, industries	As LinkedIn states them on the job page
salaryMin, salaryMax, salaryCurrency, salaryPeriod	Parsed pay (yearly / monthly / hourly; cents kept)
salarySource, salaryRaw	linkedin (LinkedIn's pay box) or description (the employer's own sentence, quoted in salaryRaw)
applicantCount, applicantCountText, applicantCountIsLowerBound, applicantCountIsUpperBound	"Over 200 applicants" is a floor, "Be among the first 25" a ceiling
descriptionText, descriptionHtml	Full description as readable text (line breaks and bullets kept) and as HTML
listingBadge, benefits	"Actively Hiring" / "Be an early applicant"; a benefits line when the listing has one (rare)
companyName, companyId, companyLinkedInUrl, companyWebsite, companyDomain	Company identity. companyWebsite is as LinkedIn lists it; companyDomain is the company's registrable domain (news.microsoft.com → microsoft.com; a campaign link such as figma.bot/… is followed to figma.com)
companyIndustry, companyEmployeeCount, companyFollowers, companyHQ, companyFounded	Company firmographics from its LinkedIn page, on every row
linkedinJobCount, linkedinJobCountIsLowerBound	LinkedIn's own count of open jobs for the company and your search ("2,000+" is a floor)
detailStatus, companyResolvedBy, query, isNew, scrapedAt	Provenance: whether the job page was read, how the company was identified, which input it came from, whether a monitor saw it before

Sample output

A real row from a platform run (description shortened):

{
  "companyName": "Vercel",
  "companyId": "16181286",
  "companyDomain": "vercel.com",
  "companyIndustry": "Software Development",
  "companyEmployeeCount": 1027,
  "companyHQ": "San Francisco, California, US",
  "companyFounded": 2015,
  "linkedinJobCount": 32,
  "jobId": "4460361914",
  "title": "Software Engineer, Financial Data Platform",
  "location": "New York, United States",
  "postedDate": "2026-09-19",
  "seniorityLevel": "Not Applicable",
  "employmentType": "Full-time",
  "jobFunction": "Other",
  "industries": "Software Development",
  "salaryMin": 190000,
  "salaryMax": 258000,
  "salaryCurrency": "USD",
  "salaryPeriod": "yearly",
  "salaryRaw": "The San Francisco, CA base pay range for this role is $190,000 - $258,000.",
  "salarySource": "description",
  "applicantCount": 25,
  "applicantCountText": "Be among the first 25 applicants",
  "applicantCountIsUpperBound": true,
  "listingBadge": "Be an early applicant",
  "descriptionText": "About Vercel:\n\nVercel is the agentic infrastructure company, …",
  "jobUrl": "https://www.linkedin.com/jobs/view/4460361914",
  "detailStatus": "ok",
  "companyResolvedBy": "slug"
}

This row also shows a limit worth knowing: the New York job quotes only the San Francisco range. salaryRaw always shows the sentence, so you can see which location a range belongs to.

Input & filters

Input	Notes
companies	LinkedIn company URL (exact), slug (stripe), numeric company ID, or name. A name becomes LinkedIn's usual slug and must match the company name on that page — otherwise the run says so instead of returning a namesake's jobs.
keywords	Applied by LinkedIn
location	Applied by LinkedIn. Empty = worldwide
datePosted	Past 24 hours / week / month — applied by LinkedIn
experienceLevel, jobType	Seniority and employment type, several allowed. Applied by the Actor to each job page (LinkedIn ignores these filters on public pages), so full details are switched on and every page read is charged as a job detail, matched or not
maxJobsPerCompany	Up to 1,000 (LinkedIn's public search stops at about 1,000 per company and search)
scrapeDetails	Read each job page (description, criteria, salary, applicants). On by default
onlyNewJobs, monitorName	Monitoring: return only jobs this monitor has not returned before

Slugs are the address of the company page, and single words are read as slugs. They can belong to a namesake: notion is a 39-person company called "Notion"; Notion Labs is notionhq. When a slug lands on a page with no jobs, the run names that page and its size. When in doubt, paste the URL.

Example inputs (copy & paste)

Engineering jobs posted this week at three companies:

{ "companies": ["stripe", "vercel", "https://www.linkedin.com/company/notionhq/"], "keywords": "engineer", "datePosted": "past-week", "maxJobsPerCompany": 100 }

All of Microsoft's open jobs in Germany, with details:

{ "companies": ["https://www.linkedin.com/company/microsoft/"], "location": "Germany", "maxJobsPerCompany": 1000 }

Weekly monitor of ten target accounts (schedule it; each run returns up to 50 jobs per company that it has not returned before — raise the number for busy employers):

{ "companies": ["stripe", "vercel", "https://www.linkedin.com/company/notionhq/", "figma", "datadog", "airbnb", "shopify", "hubspot", "snowflake-computing", "openai"], "datePosted": "past-week", "maxJobsPerCompany": 50, "onlyNewJobs": true, "monitorName": "target-accounts" }

Director and executive full-time roles only (Stripe states seniority on every job):

{ "companies": ["https://www.linkedin.com/company/stripe/"], "experienceLevel": ["director", "executive"], "jobType": ["full-time"], "maxJobsPerCompany": 20 }

Use cases

• Sales and agencies: open roles are buying signals — a company hiring five data engineers is building a data team. Salary and seniority say how big the budget is.
• Recruiters: every open role at your target clients, with pay ranges and applicant counts to prioritise.
• Investors and analysts: hiring volume (linkedinJobCount) and mix by function, seniority and country, company by company, week by week.
• Job boards and salary research: structured jobs with parsed, sourced pay.

Performance & throughput

Measured on the Apify platform (512 MB memory, residential proxy, 2026-09-24):

Run	Jobs	Time	Rate
Stripe + Vercel + Microsoft, 200 each, full details	512	342 s	≈ 90 jobs / min
Same, without details (earlier build, one company at a time)	512	214 s	≈ 144 jobs / min
Microsoft in Germany, all jobs (LinkedIn count 12), full details	12	37 s	12 / 12 delivered

• Three companies are processed at a time. Each company's job pages are read ten at a time while the next result page loads.
• 512 MB is enough: the Actor only makes HTTP requests and parses HTML, with no browser.
• LinkedIn sometimes refuses a request (HTTP 999 / 429). The Actor retries it on a new residential IP, up to four attempts. In the full-detail run above, 512 of 512 job pages were read. A job whose page could not be read is still delivered, with detailStatus unavailable or job-closed, and is not charged as a detail.

Data quality

Measured on platform runs (builds 0.1.1–0.1.4, 2026-09-24):

	Share of jobs
Title, location, posted date, job URL	100 %
Company name, ID, domain, industry, employees, HQ	100 %
Seniority, employment type, job function, industries, applicant count, description (details on)	100 % of jobs whose page was read; pages read 512 / 512
Salary (details on)	68 % of 897 unique jobs: 99 % of US-located jobs, 30 % elsewhere. By company from 0 % (Shopify, Goldman Sachs samples) to 94 % (Figma); Microsoft Germany 12 of 12
Founding year	61–100 % — some company pages state none (Microsoft), and none is invented
Unique jobs per input	100 %

How salary is read:

• LinkedIn's pay box first (salarySource: "linkedin"). Otherwise the description ("description"), but only in a sentence that names pay ("salary", "pay range", "hourly rate", …) or states a period ("/hr", "per year"), and only with a currency. A lead-in such as "The reasonably estimated yearly salary for this role is:" counts for the line under it.
• Bonus, stipend, funding and revenue amounts are not pay. Implausible values are rejected, and so are ranges wider than 8×.
• A "Pay Range" heading counts for the lines under it, and "Minimum hourly: $35.50" plus "Maximum hourly: $55.60" become one range.
• One stated amount is the exact pay (min = max); "up to $X" fills only the maximum, "starting at $X" only the minimum.
• A range in another country's currency than the job's (a London job quoting its Amsterdam range in euros) is left out.

Pricing

Pay per event:

• job — each job delivered.
• job-detail — each job page read: every delivered job with details on, plus, when you use the seniority / job-type filter, the pages of jobs the filter left out.

Companies that are not found or have no jobs cost nothing and leave an explanatory row. The current prices are on the Pricing tab.

FAQ

Why is there no remote / hybrid filter? LinkedIn's public pages ignore the workplace filter (results do not change) and do not state the workplace type reliably, so the Actor offers neither the filter nor a column rather than guess. Use keywords (for example "remote") if the word matters to you.

Why did I get fewer jobs than linkedinJobCount? LinkedIn's public search stops at about 1,000 per company and search. Split big employers by location, keywords or datePosted. linkedinJobCountIsLowerBound marks counts LinkedIn shows as "2,000+".

How do I catch every new job with a monitor? Each monitoring run returns at most "Max jobs per company" jobs the monitor has not seen, and remembers them. Set it above the company's volume for your search (linkedinJobCount on the rows shows it), and use "Posted: past week" for a weekly schedule. Measured: a second run over ten companies returned 157 jobs, none of them returned before.

Do I need a LinkedIn account or cookies? No. Only public job pages are read.

What does a seniority filter cost? LinkedIn cannot filter its public pages, so the Actor reads the page of every listed job to test it. Each page read is a job detail, matched or not. To keep a rare filter from running up a bill, it reads at most 20 listed jobs per requested job (at least 50) and says so when it stops.

Why does the seniority filter find nothing at some companies? Many employers state no seniority on LinkedIn. In our sample of twelve tech companies, six (Vercel, Microsoft, HubSpot, Snowflake, OpenAI, Notion) had "Not Applicable" on every job, while Stripe, Figma, Shopify, Airbnb and Goldman Sachs state it. When the first 20 job pages of a company all say "Not Applicable", the Actor stops that company after those 20 pages and tells you why. Run it without the seniority filter, or add "Not Applicable".

Troubleshooting

• "No LinkedIn company page at /company/…/ (made from the name …)" — the name does not match LinkedIn's slug. Paste the company URL.
• "… resolved to …" — the slug belongs to a different company than the name you gave. Paste the company URL.
• Empty result with a note about the page's size — the slug is a namesake (see notion vs notionhq above).
• Every company's outcome is in the run's SOURCE_REPORT record: status, LinkedIn's count, jobs delivered, jobs left out by filters, request counters.

Notes, limits & legal

• Reads public LinkedIn job pages only; no login, no private data. Not affiliated with LinkedIn Corporation.
• No person-level data: LinkedIn shows the hiring team only to signed-in members, and this Actor does not sign in. Job descriptions can still name people; if you store them, handle them lawfully (for example under GDPR).
• LinkedIn changes its pages from time to time; parsers are covered by regression tests on real pages.

Support

Open an issue on the Actor's Issues tab. Include the run ID; the SOURCE_REPORT record usually shows the cause.

Changelog

See ./CHANGELOG.md. 0.1 (2026-09-24): first version.