LinkedIn Company Employees Scraper

Give it a LinkedIn company and get the people who work there — filtered the way Sales Navigator filters them, with full profiles and email lookup when you need them. No cookies, no LinkedIn account, no proxies of your own, and nothing about your session is ever involved.

It finds the whole staff, not the handful of names a public company page shows, and the people come back with their names attached rather than as the "LinkedIn Member" placeholders that a plain people-search returns.

What it costs

What you get	Price per 1,000
Search result — name, profile URL, About text, location, current role and tenure	$4
Full profile — plus the whole work history, education, skills, certifications, languages, recommendations, follower counts	$8
Full profile + likely emails — plus every address that fits the company's mail domain, none of them confirmed	$12
Full profile + verified email — plus one address that was checked against the mail server and answered	$18

Each search costs $0.02 to start. One search covers up to 50 companies at once, so a run over one company and a run over fifty both pay it once — unless you ask for the companies to be searched separately, which is a search each.

You are charged for people that reach your dataset, and only for them. A person the filters exclude is never fetched. If the profile page cannot be read, the person is still stored, but as a search result and at the search-result price.

How to use it

Paste one or more LinkedIn company URLs into Companies and run it:

{
  "companies": ["https://www.linkedin.com/company/apify"],
  "profileScraperMode": "full",
  "maxItems": 100
}

Company names work too — "Stripe" instead of the URL. Each name is looked up first and the log reports which company it matched, so you can tell the company you meant from an agency with a similar name. That lookup is not charged.

Narrowing it down

Everything below is applied by LinkedIn before the results are counted, which means filtering makes a run cheaper, not more expensive:

{
  "companies": ["https://www.linkedin.com/company/stripe"],
  "jobTitles": ["Engineering Manager", "Head of Engineering"],
  "seniorityLevelIds": ["220", "300", "310"],
  "locations": ["United Kingdom"],
  "yearsAtCurrentCompanyIds": ["1", "2"],
  "excludeCurrentJobTitles": ["Intern"],
  "profileScraperMode": "full-email-verified",
  "maxItems": 200
}

Locations are resolved through LinkedIn's own autocomplete, and the log says which place actually matched. This matters more than it sounds: on LinkedIn, "UK" matches Ukraine. Write "United Kingdom".

You can also filter by past job titles, schools, industry, job function, years of experience, company size and whether someone recently changed jobs — and exclude on most of the same.

Emails: the two options are different in kind

The cheaper email option returns the addresses that fit the company's mail pattern. For one person that is typically half a dozen candidates — jakub@, jrajsky@, jakub.rajsky@ and so on — and not one of them has been confirmed to exist. Every row it produces is marked emailStatus: "unverified_guess".

The verified option checks the mailbox and returns the address that answered, marked emailStatus: "verified" with a quality score. Usually exactly one. If nothing is found the row says not_found rather than quietly dropping the field.

Both are a search for an address, not a field copied off the profile — LinkedIn does not publish work emails. Neither is guaranteed to find anything.

Big companies

A single search hands over at most 2,500 people — 100 pages of 25. That is LinkedIn's ceiling, not this Actor's, and no scraper gets past it in one query. For a company larger than that, run several searches split by location, job title, seniority or function, and the slices together cover the staff.

Start from page and Pages to scrape let you continue where a previous run stopped without paying again for the pages you already have.

What you get

A full profile row, trimmed to the interesting parts:

{
  "id": "ACoAAAYiG-8Bn3XJJeo_u5XmJz-PeB9oLx31YHM",
  "publicIdentifier": "jakub-rajsky-a135632a",
  "linkedinUrl": "https://www.linkedin.com/in/jakub-rajsky-a135632a",
  "firstName": "Jakub",
  "lastName": "Rajský",
  "headline": "Web and Product Analytics | MarTech | Data Engineering & Automation at Apify",
  "about": "My guilty pleasure from the time when I was a kid...",
  "email": "jakub.rajsky@apify.com",
  "emailStatus": "verified",
  "emails": [
    { "email": "jakub.rajsky@apify.com", "status": "valid", "deliverable": true, "qualityScore": 90 }
  ],
  "location": {
    "linkedinText": "Prague, Czechia",
    "countryCode": "CZ",
    "parsed": { "city": "Prague", "state": "Praha, Hlavní město", "country": "Czechia", "countryCode": "CZ" }
  },
  "verified": true,
  "openToWork": false,
  "hiring": false,
  "premium": false,
  "registeredAt": "2011-01-29T18:49:25.945Z",
  "connectionsCount": 1044,
  "followerCount": 1044,
  "currentPosition": [
    {
      "position": "Senior Web and Product Analyst",
      "companyName": "Apify",
      "companyId": "10608457",
      "companyLinkedinUrl": "https://www.linkedin.com/company/apify/",
      "employmentType": "Full-time",
      "workplaceType": "Hybrid",
      "duration": "2 yrs 7 mos",
      "startDate": { "month": "Feb", "year": 2024, "text": "Feb 2024" },
      "endDate": { "text": "Present" },
      "skills": ["SQL", "Snowflake", "Google Analytics 4"]
    }
  ],
  "experience": [ "...ten roles, each with company, dates, description and skills..." ],
  "education": [
    {
      "schoolName": "Prague University of Economics and Business",
      "degree": "Master's degree programme",
      "fieldOfStudy": "Information management",
      "period": "2012 - 2015"
    }
  ],
  "skills": [{ "name": "SQL", "positions": ["Senior Web and Product Analyst at Apify"] }],
  "languages": [{ "name": "Czech", "proficiency": "Native or bilingual proficiency" }],
  "certifications": [{ "title": "Python Programming Essentials", "issuedBy": "Coursera" }],
  "receivedRecommendations": [{ "givenBy": "Jan Vošický", "givenAt": "August 28, 2013" }],
  "moreProfiles": [{ "firstName": "Martina", "lastName": "Gelnerová", "position": "Senior Data Analyst at Apify" }],
  "searchedCompany": "https://www.linkedin.com/company/apify",
  "profileDepth": "profile"
}

Also on every row: projects, publications, courses, patents, honorsAndAwards, volunteering, organizations, causes, topSkills, coverPhoto, influencer, creator and memorialized.

A search result row carries the same field names with the deeper ones absent, so a short run and a full run export to the same columns. profileDepth tells you which of the two a row is.

Export as JSON, CSV, Excel or XML from the Storage tab, or pull it from the API.

What to keep in mind

• Measured against the live source, not assumed. The 2,500-per-search ceiling, the 25-per-page rate and the shape of every field above come from real responses, not from documentation.
• moreProfiles is free extra reach. LinkedIn suggests people alongside each profile, and at a company they are usually more colleagues. They arrive with a full profile at no extra cost.
• The dataset mirrors the search. If the source serves the same person on two pages, you get two rows. Nothing is silently deduplicated, because the alternative is a run that quietly drops people it should have kept.
• Set a maximum charge on the run to cap what a single run can spend. The Actor stops when it reaches it and reports what it stored.
• Search results carry the member-id form of the profile URL (/in/ACwAAA...). It resolves in a browser and identifies the person, but it is not the readable handle; the full profile modes replace it with the real one.

No cookies, no accounts

You do not supply a LinkedIn session, and none of your credentials are involved at any point. There is no risk to a LinkedIn account of yours, because none is used. Only data that LinkedIn shows publicly is collected.

Emails are discovered by searching for them, not read from a profile.

Support

Something wrong, or a filter you need that is not here? Open an issue on the Issues tab and it gets read.