LinkedIn Jobs Scraper | Remove Duplicate Jobs | Pay Per Result

LinkedIn Jobs Scraper is a LinkedIn Jobs API alternative that runs on Apify: it turns public LinkedIn job postings into clean, structured JSON, CSV or Excel data you can use anywhere. No LinkedIn account, no login and no cookies needed, and no coding. You only pay for the jobs you get (see pricing), and you get Apify's scheduling, monitoring, API access and integrations built in.

What you get for every job: title, company, location, salary (when listed), full description, job type, experience level, application link, posting date, number of applicants and, when LinkedIn shows one, the recruiter or poster. Optionally add company details (website, address, industry, employee count, specialties) at no extra cost. That is 40+ data fields per job, see the full output fields. You can also let AI answer your own questions about each job (paid plans).

At a glance

What it is	A LinkedIn jobs scraper and API alternative for public LinkedIn job listings
Search by	Keywords and locations, or LinkedIn job search URLs, or both together
Filters	Date posted, work type (remote, hybrid, on-site), job type, experience level, Easy Apply, company, plus extra filters and scoring to keep only the jobs you care about
AI job filter	Ask multiple-choice questions about each job (remote? visa sponsorship? language?) and get an AI answer per job. Paid Apify plans only (details)
Duplicates	Optional removal of duplicate jobs, so you don't pay twice
Export formats	JSON, CSV, Excel, XML, HTML, RSS
Ways to run	Apify Console, scheduled runs, API, webhooks, n8n, Make.com, Zapier
LinkedIn account needed	No: no login, no cookies, no credentials

Use it your way

• 📊 Explore it: download the results as Excel, CSV or JSON for analysis, dashboards, market research or your own job hunt.
• 🔁 Automate it: run it on a schedule or with webhooks and connect it to n8n, Make.com, Zapier, Google Sheets, Slack, Notion or a CRM, for example for job alerts, newsletters, job boards or lead lists.
• 🤖 Feed your AI agents: give fresh job data to AI agents and LLM workflows (Claude, ChatGPT, n8n agents and more) through the Apify API or Apify's MCP server, for example to tailor a resume, qualify leads or draft outreach. Built-in filters and scoring remove irrelevant jobs first, so your AI only processes what matters.
• 🛠️ Build on it: pull the data through the Apify API (Python, Node.js or cURL) into your own app, database or research pipeline.

Common uses: recruiting and talent sourcing, job hunting, hiring-trend and salary research, competitor hiring monitoring, B2B lead generation, and job boards or newsletters.

Get started in 3 steps

1. Enter keywords and a location, or paste one or more LinkedIn job search URLs.
2. Click Start.
3. Download the results or send them to your tools.

You can run many searches at once: each search returns up to 1,000 jobs, and all results land in one dataset (details). Prefer to watch first? See the video tutorials.

LinkedIn Jobs Scraper Pricing

Pay only for the jobs you get. There is no monthly fee for the Actor, and the price per 1,000 jobs goes down as your Apify plan goes up. You can try it on the Free plan, where the first jobs are covered by your free Apify credits.

Event Type	🆓 Free Tier	🥉 Bronze Tier	🥈 Silver Tier	🥇 Gold Tier
Job Result Event (per 1,000 jobs)	$0.7	$0.60	$0.50	$0.35
AI Job Analysis Event (per 1,000 analyses) *	N/A	$0.50	$0.50	$0.50

* The AI job analysis event only applies when you enable Use AI Job Description Parsing. It is charged once for each job the AI successfully analyses, however many questions you ask, and the price is the same on every tier. The AI filter is not available on the Free plan, so no AI event is charged there.

Example costs:

• Scraping 500 jobs on Free Tier: $0.35
• Scraping 5,000 jobs on Silver Tier: $2.50
• Scraping 1,000 jobs on Bronze Tier with the AI filter, if all 1,000 are analysed: $0.60 + $0.50 = $1.10

💡 Save money: enable saveOnlyUniqueItems to avoid paying for duplicate job postings. Enabling enrichCompanyData is free: it does not increase the price per result, the run just may take longer.

Good to know

• AI filter cost: the AI only analyses jobs that passed every other dynamic filter, so adding dynamic filters (for example requireSalaryInfo) lowers the number of AI events you pay for. Jobs where the AI is skipped or fails are never charged for the AI.
• Actor start fee: each run also incurs a small fixed Apify actor start fee based on the memory size you choose: 0.005 dollars per GB. The default 4GB machine costs up to 0.02 dollars per run. It is charged once per run, regardless of how many jobs are scraped.
• Actor timeout: the actor automatically terminates after 25 minutes.

LinkedIn Jobs Scraper Tutorial

Watch the official tutorials to get started quickly:

📺 How to use the LinkedIn Jobs Scraper

📺 Build a Free Automated LinkedIn Job Newsletter with Make.com & Apify

📺 Build Any Apify Actor Input with AI: No Docs, No Code

Community Tutorials

Made by the community using this actor:

• 📺 Build a LinkedIn Job Scraping & Lead Generation System with N8N, Apollo & Notion
• 📺 Scrape 1,000+ LinkedIn Jobs for 35 Cents Using Apify (Step-by-Step Guide)
• 📺 Scrape LinkedIn Jobs, Auto-Customize Your Resume with AI & Find Hiring Manager Emails (N8N)
• 📺 Turn 1,000 LinkedIn Jobs Into Sales Leads with an AI Outreach Assistant (N8N)

Who Uses a LinkedIn Jobs Scraper?

• Automation builders: schedule runs and send fresh jobs to n8n, Make.com, Zapier, Google Sheets, Slack, Notion or a CRM. Use dynamicFilterMatch and keywordMatchScorePercentage to pass only relevant jobs downstream and cut noise and processing costs.
• AI and agent developers: give your agents and LLM workflows structured job and company data (resume tailoring, lead qualification, outreach drafts). Filter first so the AI only reads jobs worth the tokens.
• Recruiters & HR professionals: build talent pipelines and track competitors' openings, salary ranges and in-demand skills.
• Job seekers: gather opportunities from many searches in one place, compare salaries and get direct application links without browsing LinkedIn manually.
• Market researchers, analysts & BI teams: analyze hiring trends, skill requirements, remote work adoption and company growth, and build dashboards over time.
• Developers & sales teams: pull job and company data through the API to power your own product, or to find companies that are hiring.

Key benefits:

• ✅ Pay only for results: you are charged for the jobs you get, with no monthly fee for the Actor.
• ✅ No coding required: a simple form with clear options, or the API if you prefer code.
• ✅ Duplicate prevention: an optional unique-only mode saves you money.
• ✅ Rich company data: optional enrichment adds company details, addresses and employee counts at no extra charge.
• ✅ Flexible search: use LinkedIn URLs or keywords, or combine both.
• ✅ Ready-to-use data: JSON, CSV or Excel exports.

FAQ

Will my LinkedIn account get blocked or banned?

No. The scraper only accesses LinkedIn's public job pages — the same pages anyone can view without logging in. Your LinkedIn account is never used or required.

Can I scrape LinkedIn jobs without logging in or using a LinkedIn account?

Yes. The scraper only reads publicly available job listings, the same pages anyone can open without an account. No login, no cookies, no credentials and no LinkedIn account are needed.

Is it legal to scrape LinkedIn job postings?

Yes. This scraper only collects data from LinkedIn's public pages that are accessible to anyone without an account. It does not bypass any login or access private data.

Is there a free LinkedIn jobs scraper?

You can try it for free on Apify's Free plan, which comes with free monthly platform credits, and there is no flat monthly fee for the Actor. Beyond that you only pay for the jobs you get, see the Pricing table. The AI filter needs a paid Apify plan.

How much does it cost to scrape LinkedIn jobs?

You pay per 1,000 jobs, and the price depends on your Apify plan. See the Pricing table. There is no flat monthly fee for the Actor.

How many jobs can I scrape?

Up to 1,000 jobs per search (per keyword/location combination or per LinkedIn search URL). This is a LinkedIn limitation, not the scraper's, and it is not a limit per run. Put several keywords, locations or URLs in one run to collect thousands of jobs (subject to maxItems). See How to Scrape LinkedIn Jobs for a concrete example.

Do I need coding skills to use this?

No. Everything is configured through a simple form on Apify. Just fill in your keywords, location, and filters and click Run.

Is there a LinkedIn jobs scraper API?

Yes. Every Apify Actor can be started and read through the Apify API, so you can trigger runs and fetch the results from your own app, script, automation or AI agent. This Actor is a LinkedIn Jobs API alternative: LinkedIn has no public jobs API, and this one returns the public job listings as structured JSON.

Can I scrape LinkedIn jobs with Python?

Yes. Use the Apify Python client (pip install apify-client) to run the Actor and read the results:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run = client.actor("cheap_scraper/linkedin-job-scraper").call(
    run_input={
        "keyword": ["Python Developer"],
        "locations": ["Montreal, Canada"],
        "publishedAt": "r604800",
        "saveOnlyUniqueItems": True,
        "maxItems": 100,
    }
)

for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(job["jobTitle"], "-", job["companyName"])

A JavaScript client (apify-client on npm) and a plain REST API are also available.

Is there a LinkedIn jobs scraper on GitHub or a Chrome extension?

You can find open-source scripts on GitHub, but you have to host, maintain and fix them yourself when LinkedIn changes. This Actor runs in the cloud on Apify, so there is nothing to install: no browser extension, no code to maintain and no use of your LinkedIn account, which also means no risk to it.

Can I export the results to Excel or Google Sheets?

Yes. Apify lets you download your results in CSV, Excel, or JSON format directly from the dashboard — no coding required.

Can I run it automatically on a schedule?

Yes. Apify has a built-in scheduler. Set it to run daily, weekly, or at any interval and get fresh job data automatically without any manual work.

Can I connect it to Make.com, Zapier, or n8n?

Yes. Apify integrates natively with Make.com, Zapier, and n8n so you can automatically send job results to Google Sheets, Slack, a CRM, or any other tool in your workflow.

Can I use it with AI agents or my own code?

Yes. Run it and fetch the results through the Apify API, so your AI agents, scripts and apps can use the data directly.

Can I scrape jobs from specific companies only?

Yes. Use the companyInclude input field to restrict results to a list of company names. The scraper will resolve each name to a LinkedIn company ID and return only their job postings.

How to Scrape LinkedIn Jobs

The LinkedIn job scraper allows you to collect jobs in 2 ways:

1. By providing one or more start URLs, or
2. By entering keyword-based search queries.

You can use either method individually or combine both.

The 1,000-job limit applies per individual LinkedIn search, NOT per Actor run. For example, ONE run with these 3 searches:

Search	Jobs returned (max)
"Software Engineer" in Toronto	up to 1,000
"Software Engineer" in Montreal	up to 1,000
"Data Scientist" in Vancouver	up to 1,000
Total in one run	up to 3,000

The total can be lower if the same job appears in several searches and saveOnlyUniqueItems is enabled (it is saved only once), or if you set a lower maxItems.

What happens when you combine several searches in one run?

• Every startUrls entry is its own search (up to 1,000 jobs each).
• keyword + locations generate additional searches (one per keyword/location combination). They never modify your startUrls.
• All results from all searches are collected in the same dataset.
• With saveOnlyUniqueItems: true, a job found by several searches is saved only once.

Example: 3 URLs in startUrls + 2 keywords × 2 locations = 7 searches in one run, all merged into one dataset.

Three kinds of filters, three different behaviors:

Type	Effect	Billed?
Keyword Search Filters (LinkedIn search filters)	Change which jobs LinkedIn returns	Only returned jobs are scraped
Universal Filters (subLocationExclude, companyExclude, ...)	Skip jobs before their page is fetched	Skipped jobs are not billed
Dynamic Filters	Flag jobs after they are fetched (dynamicFilterMatch)	Every scraped job is saved and billed

LinkedIn AI-Powered Search Mode

⚠️ Important update: LinkedIn changed its search, see the video explaining the update.

LinkedIn randomly serves either its classic search engine or a newer AI-powered search, regardless of whether you use URL-based or keyword-based search.

• Classic search: all filters (workType/jobType/experienceLevel, plus the ones listed in Keyword Search Filters below) are enforced directly.
• AI-powered search: LinkedIn ignores workType/jobType/experienceLevel as filters. The scraper compensates by rewriting your search query in plain language instead (e.g. "Remote Software Engineer"), since LinkedIn's AI search understands intent phrased this way.

How AI Search Query Rewriting Works

Only the keywords part of the search changes; location, time posted, and sort order are untouched. Where the rewrite gets its values depends on your search mode:

• Keyword-based search: uses the workType/jobType/experienceLevel field values you provided.
• URL-based search: independent of any input fields. It's based on whatever f_WT/f_JT/f_E parameters are already present in the URL (e.g. because you applied those filters on linkedin.com before copying it).

The rule (applied independently, per filter):

• 1 value selected for that filter → bare label (e.g. Remote)
• 2+ values selected for that filter → (Value1 OR Value2 OR ...)

Assembly order: [workType fragment] [jobType fragment] [experienceLevel fragment] [original keywords]

workType	jobType	experienceLevel	Rewritten query
Remote	—	—	Remote software engineer
Remote, Hybrid	—	—	(Remote OR Hybrid) software engineer
—	Full-time, Contract	—	(Full-time OR Contract) software engineer
—	—	Entry level, Associate	(Entry level OR Associate) software engineer
Remote	—	Entry level, Internship, Associate	Remote (Entry level OR Internship OR Associate) software engineer

If you don't want the actor to rewrite your query:

• Keyword-based search: leave workType/jobType/experienceLevel empty and phrase the intent directly in keyword (e.g. "Remote Entry Level Software Engineer").
• URL-based search: don't apply those filters on linkedin.com before copying the URL, phrase the intent in the LinkedIn search box instead, so the copied URL has no f_WT/f_JT/f_E params for the scraper to react to.

Input

The LinkedIn scraper's input is divided into 7 sections:

🔍 Search

Use this section to tell the scraper where to start. You can provide LinkedIn job search URLs directly, use keywords to let the scraper build the URLs for you, or combine both.

📺 Watch this section explained in the tutorial video

How to get a LinkedIn job search URL:

1. Open a private/incognito browser window (to avoid personalised results)
2. Go to https://www.linkedin.com/jobs
3. Search for a job title and apply any filters you want
4. Copy the URL from the address bar and paste it in the Start URLs field

Input name	Type	Description / Behavior	Example
startUrls	{ url: string }[]	One or more LinkedIn Job Search URLs to start crawling from. Use this when you want to set filters directly on LinkedIn in your browser. See the URL generation instructions above.	[{ "url": "https://www.linkedin.com/jobs/search?keywords=Developer&location=Canada&f_TPR=r604800" }]
keyword	string[]	Job titles or search terms for keyword-based search. Combined with locations to generate one search URL per keyword/location combination.	["Software Engineer", "Data Scientist"]

📍 Keyword Search Filters

These filters are sent directly to LinkedIn's search engine as part of the search URL. They only apply to keyword-based search and have no effect on URL-based search.

📺 Watch this section explained in the tutorial video

Input name	Type	Description / Behavior	Example
locations	string[]	Locations to search in. Each location is paired with every keyword to generate one search URL per combination. Preferred over the legacy location field.	["Montreal, Canada", "Toronto, Canada"]
location	string	(Legacy) Single location string. Merged with locations when building searches. Use locations instead.	"Montreal, Canada"
distance	string	Search radius in miles. Accepted values: "" (default), "5", "10", "15", "25", "50".	"25"
publishedAt	string	Filter by posting date. Accepted values: "" (any time), "r86400" (last 24 h), "r604800" (last 7 days), "r2592000" (last 30 days).	"r604800"
jobType	string[]	Filter by employment type. Multiple values allowed. Accepted values: "full-time", "part-time", "contract", "temporary", "internship", "volunteer".	["full-time", "contract"]
experienceLevel	string[]	Filter by experience level. Multiple values allowed. Accepted values: "internship", "entry-level", "associate", "mid-senior", "director", "executive".	["entry-level", "associate"]
workType	string[]	Filter by work location type. Multiple values allowed. Accepted values: "on-site", "remote", "hybrid". Search filter only, sent to LinkedIn. Not related to the output field workType.	["remote", "hybrid"]
companyInclude	string[]	Restrict the search to specific companies by name. Each name is resolved to a LinkedIn company ID — unresolved names are silently skipped.	["Google", "Shopify"]
filterUnder10Applicants	boolean	When true, only returns jobs with fewer than 10 applicants.	true
filterEasyApply	boolean	When true, only returns Easy Apply jobs (apply directly on LinkedIn without leaving the site).	true

🤖 AI search note: see 🤖 AI-Powered Search Mode above certain filters in this table stop working as real filters when LinkedIn serves its AI-powered search, and the scraper compensates with a query rewrite instead.

🔽 Universal Filters

These filters apply to both keyword-based and URL-based searches. Jobs that match an exclusion rule are skipped entirely and are not fetched or billed.

📺 Watch this section explained in the tutorial video

Input name	Type	Description / Behavior	Example
subLocationExclude	string[]	Skip jobs from specific cities or sub-areas. Any job whose location string contains the provided name (case-insensitive substring) is excluded before being fetched.	["Laval", "Longueuil"] — skips any job in "Laval, Quebec, Canada"
companyExclude	string[]	Skip jobs from specific companies. Exact case-insensitive match against the company name on the job card.	["Randstad", "Adecco"]
jobTitleExclude	string[]	Skip jobs whose title contains any of these keywords (whole-word, case-insensitive). "senior" excludes "Senior Software Engineer" but not "seniority".	["senior", "manager", "director"]

🔎 Dynamic Filters

📺 Watch this section explained in the tutorial video

⚠️ These are NOT LinkedIn search filters — they do not change which jobs LinkedIn returns to you. Jobs are always fetched first and billed, then evaluated against your criteria.

Works equally for both search modes. Because filtering happens after the job page is fetched, it applies regardless of whether you used URL-based search or keyword-based search.

Every job is still saved to the dataset. When a job does not match your dynamic filter criteria, it is still written to the dataset but the field dynamicFilterMatch will be set to false. Jobs that pass (or when no filters are set) will have dynamicFilterMatch set to true.

⚠️ Dynamic filters do NOT prevent jobs from being scraped or billed. To keep only matching jobs in Make.com, n8n, Zapier, Google Sheets or any other tool, filter the dataset on dynamicFilterMatch = true.

One field for all filters. If you configure several dynamic filters, you only need to check dynamicFilterMatch once: it is the combined result of all of them (a job must pass every configured filter to get true).

Example: with { "excludeRecruitingAgencies": true, "requireSalaryInfo": true }, a job from a "Staffing and Recruiting" company and a job with no salary both get dynamicFilterMatch: false. Only a job from a non-staffing company that lists a salary gets true.

How excludeRecruitingAgencies works: it flags a job with dynamicFilterMatch: false only when the company's LinkedIn industry (companyIndustry) is exactly "Staffing and Recruiting". Any other industry passes, and it does not analyze the company name, description or job title. For example, a company named "ABC Staffing" whose LinkedIn industry is "Human Resources Services" is not excluded. If the company industry is unavailable, the job passes.

How jobIndustryExclude works: it checks the job's industry (the output field sector), not the company's. Several values are allowed, and a match on any of them gives dynamicFilterMatch: false:

{
  "jobIndustryExclude": ["Retail Apparel and Fashion", "Advertising Services"]
}

Industry names must match LinkedIn's classification names exactly.

Input name	Type	Description / Behavior	Example
jobIndustryInclude	string[]	Keep only jobs whose industry matches one of these values. Takes priority over jobIndustryExclude when both are set. Values must match LinkedIn names exactly (e.g. "Software Development"). You can find all the Linkedin industries at full list	["Software Development", "IT Services and IT Consulting"] — only those industries get dynamicFilterMatch: true
jobIndustryExclude	string[]	Flag jobs whose industry (the output field sector, i.e. the job's industry, not the company's) matches any of these values as dynamicFilterMatch: false. Ignored when jobIndustryInclude is also set. You can find all the Linkedin industries at full list	["Staffing and Recruiting"] — agency-posted jobs get dynamicFilterMatch: false
excludeRecruitingAgencies	boolean	When true, jobs whose company has the LinkedIn industry "Staffing and Recruiting" (companyIndustry) are flagged with dynamicFilterMatch: false. It does not analyze the company name or description. Automatically enables enrichCompanyData.	true — result: { "companyIndustry": "Staffing and Recruiting", "dynamicFilterMatch": false }
companySizeMin	number	Flag jobs from companies with fewer employees than this value as dynamicFilterMatch: false. Companies with no employee count on LinkedIn always pass. Automatically enables enrichCompanyData.	50 — companies with fewer than 50 employees get dynamicFilterMatch: false
companySizeMax	number	Flag jobs from companies with more employees than this value as dynamicFilterMatch: false. Companies with no employee count on LinkedIn always pass. Automatically enables enrichCompanyData.	500 — to target mid-size companies combine with companySizeMin: { "companySizeMin": 50, "companySizeMax": 500 }
companyOrganizationTypeInclude	string[]	Keep only jobs from companies whose company page organization type matches one of these values. Takes priority over companyOrganizationTypeExclude when both are set. Automatically enables enrichCompanyData. If unavailable, the job always passes. Accepted values: "Public Company", "Educational", "Self Employed", "Government Agency", "Non Profit", "Self Owned", "Privately Held", "Partnership". See full list	["Privately Held", "Public Company"]
companyOrganizationTypeExclude	string[]	Flag jobs from companies whose company page organization type matches any of these values as dynamicFilterMatch: false. Ignored when companyOrganizationTypeInclude is set. Automatically enables enrichCompanyData. If unavailable, the job always passes. See full list	["Self Employed", "Self Owned"]
companyFoundedDateMin	number	Keep only jobs from companies founded in or after this year. Automatically enables enrichCompanyData. If the founding year is unavailable, the job always passes.	2010
companyFoundedDateMax	number	Keep only jobs from companies founded in or before this year. Automatically enables enrichCompanyData. If the founding year is unavailable, the job always passes. Combine with companyFoundedDateMin to target a range.	2020 — to target companies founded between 2010–2020: { "companyFoundedDateMin": 2010, "companyFoundedDateMax": 2020 }
companyFollowersCountMin	number	Keep only jobs from companies with at least this many LinkedIn followers. Automatically enables enrichCompanyData. If the follower count is unavailable, the job always passes.	10000
companyFollowersCountMax	number	Keep only jobs from companies with at most this many LinkedIn followers. Automatically enables enrichCompanyData. If the follower count is unavailable, the job always passes.	500000
requireRecruiterProfile	boolean	When true, only jobs that have a recruiter/poster profile associated with the posting get dynamicFilterMatch: true. It only checks that a poster is shown; it does not verify the person's hiring authority. Jobs without a poster are still collected and billed, but get dynamicFilterMatch: false.	true
requireSalaryInfo	boolean	When true, only jobs that include salary information in the posting get dynamicFilterMatch: true. Jobs without salary info are still collected and billed.	true

🤖 AI Dynamic Filter (Apify paid plans only)

⚠️ This input is for premium (paid Apify plan) users only. On a free plan, the AI filter is not applied: a warning is logged before scraping and every job gets aiStatus: "skipped_free_plan".

Turn on Use AI Job Description Parsing (useAiJobDescriptionParsing, default false) to use aiQuestions. The input form comes with an example already filled in, so you can see the format. The questions are only used when the option is enabled; otherwise they are ignored and no AI is used.

aiQuestions asks the AI multiple-choice questions about each job description. Like the other dynamic filters, it does not change which jobs are fetched or billed. Add expected values to also use a question as a filter: if the AI picks another choice, the job gets dynamicFilterMatch: false. Questions without expected only add information to the output.

{
  "useAiJobDescriptionParsing": true,
  "aiQuestions": [
    {
      "id": "workModel",
      "question": "Is this job remote, hybrid or on site?",
      "choices": [
        "remote | Work fully from home",
        "hybrid | Mix of office and home",
        "on-site | Work fully at the employer's location",
        "unknown | The description does not say"
      ],
      "expected": ["remote", "hybrid"]
    },
    {
      "id": "visaSponsorship",
      "question": "Does the employer offer visa sponsorship?",
      "choices": [
        "yes | The description says visa sponsorship or work permit support is offered",
        "no | The description says sponsorship is not offered or work authorization is required",
        "unknown | The description does not mention it"
      ],
      "expected": ["yes"]
    },
    {
      "id": "language",
      "question": "Which languages must the candidate speak?",
      "choices": [
        "english only",
        "french only",
        "english and french | Fluency in both languages is required",
        "other | Any other language requirement",
        "unknown | The description does not say"
      ]
    }
  ]
}

What each question does in this example:

• workModel (filter): only remote and hybrid jobs keep dynamicFilterMatch: true. on-site and unknown jobs get false.
• visaSponsorship (filter): only jobs where the AI answers yes keep dynamicFilterMatch: true. Jobs answering no or unknown get false. To also keep jobs that don't mention it, use "expected": ["yes", "unknown"].
• language (information only): there is no expected, so it never changes dynamicFilterMatch. It only adds the language requirement to aiAnswers, which is handy for sorting or filtering the dataset yourself.

A job must pass both filter questions to keep dynamicFilterMatch: true. For one job, the output looks like this:

{
  "dynamicFilterMatch": false,
  "aiStatus": "answered",
  "aiAnswers": [
    {
      "id": "workModel",
      "question": "Is this job remote, hybrid or on site?",
      "answer": "remote",
      "probability": 0.94,
      "matched": true
    },
    {
      "id": "visaSponsorship",
      "question": "Does the employer offer visa sponsorship?",
      "answer": "unknown",
      "probability": 0.81,
      "matched": false
    },
    {
      "id": "language",
      "question": "Which languages must the candidate speak?",
      "answer": "english only",
      "probability": 0.88,
      "matched": null
    }
  ]
}

• Answer choices with descriptions: each choice is one line written as answer | optional description. The part before the first | is the answer the AI can pick, and the optional part after it tells the AI what the answer means, which helps with ambiguous options. Without a |, there is no description. An answer itself cannot contain |. expected lists the answer texts only.
• Only runs when useful: the AI runs only on jobs that passed every other dynamic filter, since it can only exclude jobs. Jobs that already failed get aiStatus: "skipped".
• Combined result: every question with expected must match, and the result is merged into the single dynamicFilterMatch field.
• No automatic "unknown": the AI must pick one of your choices. Add an unknown choice yourself if the description may not answer the question, and leave it out of expected to exclude those jobs (or include it to keep them).
• Stable id: every answer carries the id you chose, so you can rename a question without breaking automations that read the answer.
• If the AI call fails: the job is kept, aiStatus is "failed", and dynamicFilterMatch only reflects the other filters.
• Billing: each job the AI successfully analyses is charged one ai-job-analysis event (see Pricing), however many questions you ask. A job is only charged after the AI answered, so jobs with aiStatus "skipped", "skipped_free_plan", "skipped_budget_limit" or "failed" are never charged for the AI. Once your maximum charge per run is reached, the Actor stops. Jobs that were still being processed at that moment may not be saved.
• Limits (an invalid question stops the run before any scraping):

Item	Limit
Questions	1 to 10
id	Required, unique, max 40 characters (letters, numbers, _, -)
question	Max 200 characters
choices	2 to 10 lines written as answer | optional description; answer max 50 characters, no duplicates; description max 200 characters
expected	Optional, each value must be one of the answers (answer text only)
Job description size	Only the first 15,000 characters are analysed

📊 Job Match Scoring

📺 Watch this section explained in the tutorial video

⚠️ This is NOT a search filter — it does not change which jobs LinkedIn returns to you.

This is a profile matching tool for smarter job hunting. Paste the skills from your resume/CV here, and the scraper will automatically score every job description against your profile. Instead of reading through dozens of postings, you instantly see which jobs you're actually qualified for — and can skip the rest entirely.

Input name	Type	Description / Behavior	Example
resumeKeywords	{ keyword: string, aliases?: string[] }[]	Array of skills from your resume/CV. Each entry requires a keyword and optionally aliases for abbreviations or alternate names. Matching is case-insensitive with smart word boundaries — "JS" won't match "JSON", "C++" is handled correctly. When set, every output job gains three extra fields: matchedKeywords (found skills), unmatchedKeywords (missing skills), and keywordMatchScorePercentage (0–100 score, computed as round(matched ÷ total × 100)).	[{ "keyword": "JavaScript", "aliases": ["JS"] }, { "keyword": "Python" }]

Full example:

[
  { "keyword": "JavaScript", "aliases": ["JS"] },
  { "keyword": "Kubernetes", "aliases": ["K8s"] },
  { "keyword": "C++" },
  { "keyword": "Project Management", "aliases": ["PM"] },
  { "keyword": "Python" }
]

Matching is case-insensitive and uses smart word boundaries — for example, JS won't accidentally match JSON, and C++ is handled correctly despite containing symbols.

🤖 Generate Skills from Your CV Automatically

Instead of manually listing your skills, let an AI extract them from your CV and output them in the exact format above:

LLM	Link	Note
ChatGPT	Open with prompt →	No login needed
Claude	Open with prompt →	Login required

Or copy this prompt and paste it into any LLM of your choice:

Extract skills and keywords from my CV for job matching. Add common abbreviations as aliases.
Output ONLY a raw JSON array with no explanation or markdown, using this format:
[{"keyword":"JavaScript","aliases":["JS"]},{"keyword":"Python"}].
I will paste my CV after this message.

The AI will output a JSON array — copy and paste it directly into the resumeKeywords field.

⚙️ Run Settings

General settings that control how the scraper runs. These apply to both URL-based and keyword-based search.

📺 Watch this section explained in the tutorial video

Input name	Type	Description / Behavior	Example
maxItems	number	Maximum number of job listings to scrape across all searches in the run (not per URL). Example: maxItems: 500 with five search URLs gives 500 results in total, not 500 per URL. Leave empty for no limit.	500
saveOnlyUniqueItems	boolean	When true, the same LinkedIn job ID is saved only once within a single run, including duplicates found across different search URLs, keywords and locations. Duplicates are skipped entirely (not flagged, not fetched, not billed). Deduplication does not persist across separate runs: to avoid duplicates across runs, store the jobId values you already have and compare new results against them. Default is false.	true
enrichCompanyData	boolean	When true, visits each company's LinkedIn page to fetch extra fields: companyDescription, companyAddress, companyEmployeeCount, companyEmployeeCountRange, companyWebsite, companyIndustry, companyOrganizationType, companyFoundedDate, companySpecialties, companyFollowersCount, companyOfficeLocations, companyAffiliatedPages, and companyRecentPosts. Default is false. Included at no additional charge: it does not change the price per result, the only trade-off is a longer run time because company pages are visited.	true

⚠️ Deprecated Filters

Job Function filters (jobFunctionInclude, jobFunctionExclude) are no longer usable. LinkedIn no longer exposes the job function, so it cannot be scraped and the output field workType is always empty. Do not use these two filters.

The other deprecated filters below are currently unreliable. They may or may not work depending on the search engine LinkedIn serves. When LinkedIn serves its new AI-powered search, these filters are silently ignored. They will be restored once LinkedIn supports them again. Using them will not cause any errors, but results may include jobs that do not match the filter.

Input name	Type	Description / Behavior	Example
salaryBase	string	Deprecated - currently only works when LinkedIn serves its classic search engine; in AI-powered search LinkedIn ignores it and it cannot be verified after scraping. Minimum salary filter. Accepted values: "" (any), "40000", "60000", "80000", "100000", "120000".	"80000"
jobFunctionInclude	string[]	Deprecated, no longer usable. LinkedIn no longer exposes the job function on public job pages, so it cannot be scraped (the output field workType is always empty). Using it flags every job with dynamicFilterMatch: false.	["Engineering"]
jobFunctionExclude	string[]	Deprecated, no longer usable. Same reason as above: the job function cannot be scraped, so this filter has no effect (no job is ever flagged).	["Sales"]

Input Sample

{
  "enrichCompanyData": true,
  "keyword": ["Full Stack Developer"],
  "locations": ["Montreal, Canada"],
  "publishedAt": "r86400",
  "resumeKeywords": [
    {
      "aliases": ["TS", "JS", "JavaScript"],
      "keyword": "TypeScript"
    },
    {
      "aliases": ["Node", "NodeJS"],
      "keyword": "Node.js"
    },
    {
      "aliases": ["RN", "React Native", "Expo", "ReactJS"],
      "keyword": "React"
    },
    {
      "aliases": ["PostgreSQL"],
      "keyword": "SQL"
    },
    {
      "aliases": [
        "Git",
        "Docker",
        "Jira",
        "Postman",
        "GitHub",
        "GitLab",
        "GitHub Copilot",
        "Webpack"
      ],
      "keyword": "Tools"
    }
  ],
  "saveOnlyUniqueItems": true,
  "jobTitleExclude": ["senior", "manager", "director", "lead", "principal"]
}

Note: You can use either startUrls or keyword (with locations), or combine both methods in a single run.

LinkedIn Job Data You Can Export

Output Fields

Field	Type	Description
jobId	string | null	Unique LinkedIn job identifier.
jobTitle	string | null	Job position title.
location	string | null	Job location as text (e.g. "Montreal, Quebec, Canada").
salaryInfo	string[]	Salary range as formatted strings (e.g. ["$69100.00", "$129200.00"]). Empty array if not listed.
postedTime	string | null	Human-readable relative posted time (e.g. "2 days ago").
publishedAt	string | null	ISO 8601 date when the job was published (e.g. "2026-06-14T00:00:00.000Z").
searchString	string | null	The search query that found this job (e.g. "Software Engineer - Montreal, Canada").
jobUrl	string | null	LinkedIn job posting URL.
companyName	string | null	Company name.
companyUrl	string | null	LinkedIn company page URL (not the company's own website, see companyWebsite).
companyLogo	string | null	Company logo image URL.
companyId	string | null	LinkedIn internal company identifier.
jobDescription	string | null	Full job description as plain text.
applicationsCount	string | null	Number of applications received (e.g. "Over 200 applicants").
contractType	string | null	Employment type (e.g. "Full-time", "Contract").
experienceLevel	string | null	Required experience level as listed on the posting (e.g. "Mid-Senior level").
yearsOfExperience	{ years: string, context: string, lang: string }[]	Structured experience requirements extracted from the job description. years is a normalized value (e.g. "5+", "2-5"). Empty array if none found.
workType	string | null	Deprecated, always empty. It used to contain the job function (e.g. "Engineering and Information Technology"), but LinkedIn no longer exposes it. Not related to the input workType filter (remote/hybrid/on-site). The Job Function dynamic filters depend on it and are deprecated too.
sector	string | null	Industry sector of the job (e.g. "Software Development").
applyUrl	string | null	URL to apply for the job. May differ from jobUrl for Easy Apply jobs.
applyType	string | null	Application method: "EASY_APPLY" (apply on LinkedIn) or "EXTERNAL" (redirects to company site).
posterFullName	string | null	Name of the person associated with the job posting, when LinkedIn shows one. May be a recruiter, hiring manager or another employee; it is not verified to be the hiring decision-maker. Empty string if not shown.
posterProfileUrl	string | null	LinkedIn profile URL of the person associated with the posting (see posterFullName). Empty string if not shown.
dynamicFilterMatch	boolean	true when no dynamic filters are configured or all configured filters passed. false when the job did not meet one or more filter criteria — the job is always saved regardless.
matchedKeywords	string[]	Skills from resumeKeywords that were found in the job description. Only present when resumeKeywords is set.
unmatchedKeywords	string[]	Skills from resumeKeywords that were NOT found in the job description. Only present when resumeKeywords is set.
keywordMatchScorePercentage	number | null	Percentage of resume skills matched (0–100). Computed as round(matched ÷ total × 100). Only present when resumeKeywords is set.
aiStatus	string	State of the AI filter: "answered", "skipped" (no aiQuestions, or the job already failed the other filters), "skipped_free_plan" (AI filter needs a paid plan), "skipped_budget_limit" (your maximum charge was reached while the job was in progress, so the AI was not run) or "failed" (AI call failed, job kept).
aiAnswers	{ id: string, question: string, answer: string | null, probability: number | null, matched: boolean | null }[]	One entry per aiQuestions item, in the same order. probability (0–1) is how sure the AI was of its answer — a confidence signal, not guaranteed accuracy; null if not returned. matched is null for questions without expected values. Empty unless aiStatus is "answered".
companyDescription	string | null	Company description from the LinkedIn About section. Only present when enrichCompanyData: true
companyAddress	object | null	Headquarters address with fields: streetAddress, addressLocality, addressRegion, postalCode, addressCountry. Only present when enrichCompanyData: true.
companyEmployeeCount	number | null	Exact employee count as shown on LinkedIn (e.g. 1019). Only present when enrichCompanyData: true.
companyEmployeeCountRange	string | null	Employee count range label (e.g. "201-500 employees"). Only present when enrichCompanyData: true.
companyWebsite	string | null	External website of the company, as listed on its LinkedIn company profile (unlike companyUrl, which is the LinkedIn page). Empty if the company has not provided one. Only present when enrichCompanyData: true.
companyIndustry	string | null	Industry category from the LinkedIn About section (e.g. "Software Development"). Only present when enrichCompanyData: true.
companyOrganizationType	string | null	Organisation type (e.g. "Privately Held", "Public Company"). Only present when enrichCompanyData: true.
companyFoundedDate	string | null	Year the company was founded (e.g. "2019"). Only present when enrichCompanyData: true.
companySpecialties	string[]	List of company specialties from the About section. Only present when enrichCompanyData: true.
companyFollowersCount	number | null	Number of LinkedIn followers the company has. Only present when enrichCompanyData: true.
companyOfficeLocations	string[]	List of office address strings from the company page. Only present when enrichCompanyData: true.
companyAffiliatedPages	string[]	Names of affiliated LinkedIn company pages. Only present when enrichCompanyData: true.
companyRecentPosts	{ text: string, datePublished: string, url: string }[]	Recent posts published by the company on LinkedIn. datePublished is ISO 8601. Only present when enrichCompanyData: true.

[
  {
    "jobId": "4354494117",
    "jobTitle": "Full-Stack Software Engineer, Inference",
    "location": "Montreal, Quebec, Canada",
    "salaryInfo": [],
    "postedTime": "6 hours ago",
    "publishedAt": "2026-06-14T00:00:00.000Z",
    "searchString": "Full Stack Developer - Montreal, Canada",
    "jobUrl": "https://ca.linkedin.com/jobs/view/full-stack-software-engineer-inference-at-cohere-4354494117?trk=public_jobs_topcard-title",
    "companyName": "Cohere",
    "companyUrl": "https://ca.linkedin.com/company/cohere-ai?trk=public_jobs_topcard-org-name",
    "companyLogo": "https://media.licdn.com/dms/image/v2/C560BAQGI_GYoDekJsQ/company-logo_200_200/company-logo_200_200/0/1680024442682/cohere_ai_logo?e=2147483647&v=beta&t=YjvCYqodFatkvEoqaggfjwCzFvzvDUFjIo5zpvlogxk",
    "jobDescription": "Who are we?\n\nCohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.\n\nWe’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.\n\nWe obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.\n\nWe are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us!\n\nWhy this role?\n\nCohere customers self-serve our API without any intervention. This team unlocks the complex technology we build for customers to understand, trust, and pay for.\n\nAs a Senior Software Engineer, You Will\n\nImprove the platform’s auth, billing, and payment systems\nAdd new features to the interactive Playground where customers can try our models\nImplement new platform features for managing deployments\nWrite and ship minimal code that runs in low-resource environments, and has highly stringent deployment mechanisms\nAs security and privacy are paramount, you will sometimes need to reinvent the wheel, and won’t be able to use the most popular libraries or tooling\n\nYou May Be a Good Fit If\n\nYou have 5+ years of experience writing clean backend code. Our stack includes: Golang and React.\nYou’ve built payment systems and have experience with subscription or usage-based SaaS, and/or products with a freemium model.\nYou have strong coding abilities and are comfortable working across the stack. You’re able to read and understand, and even fix issues outside of the main code base.\nYou’ve worked in both large enterprises and startups.\nYou excel in fast-paced environments and can execute while priorities and objectives are a moving target.\n\nHow And Where We Work\n\nCohere is remote-friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming soon.\nFor those in the office: a daily lunch program, plenty of snacks, and regular community and social events.\nFor those not near an office: a co-working benefit so you can work alongside others in your city.\n\nIf any of the above doesn’t line up exactly with your experience, we still encourage you to apply.\n\nWe strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.\n\nWe may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. \nShow more Show less",
    "applicationsCount": "Over 200 applicants",
    "contractType": "Full-time",
    "experienceLevel": "Not Applicable",
    "yearsOfExperience": [
      {
        "years": "5+",
        "context": "writing clean backend code",
        "lang": "en"
      }
    ],
    "workType": "",
    "sector": "Software Development",
    "posterFullName": "",
    "posterProfileUrl": "",
    "companyId": "24024765",
    "applyUrl": "https://ca.linkedin.com/jobs/view/full-stack-software-engineer-inference-at-cohere-4354494117?trk=public_jobs_topcard-title",
    "applyType": "EXTERNAL",
    "dynamicFilterMatch": true,
    "matchedKeywords": [
      "React",
      "Tools"
    ],
    "unmatchedKeywords": [
      "TypeScript",
      "Node.js",
      "SQL"
    ],
    "keywordMatchScorePercentage": 40
  },
  ...
]

With enrichCompanyData: true

When you enable enrichCompanyData, each result gets the following additional fields fetched from the company's LinkedIn page:

{
 "companyAddress": {
		"streetAddress": "171 John St",
		"addressLocality": "Toronto",
		"addressRegion": "Ontario",
		"postalCode": "M5T 1X3",
		"addressCountry": "CA"
	},
	"companyDescription": "Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation models and end-to-end AI products designed to solve real-world business problems. We partner closely with companies to deliver seamless integration, full customization, and easy-to-use solutions for their workforce and customers. Our all-in-one platform offers enterprises the highest levels of data security, privacy and optionality to deploy across all major cloud providers, private cloud environments, or on-premises. \n\nHQ: 171 John Street, 2nd Floor, Toronto, ON M5T 1X3",
	"companyEmployeeCount": 1019,
	"companyWebsite": "https://cohere.com",
	"companyIndustry": "Software Development",
	"companyEmployeeCountRange": "201-500 employees",
	"companyOrganizationType": "Privately Held",
	"companyFoundedDate": "2019",
	"companySpecialties": [
		"Natural Language Processing",
		"Machine Learning",
		"and Artificial Intelligence"
	],
	"companyFollowersCount": 253257,
	"companyOfficeLocations": [
		"171 John St 2nd Floor Toronto, Ontario M5T 1X3, CA",
		"San Francisco, California, US",
		"London, GB",
		"New York, NY, US",
		"Paris, FR",
		"Montreal, CA",
		"Seoul , KR"
	],
	"companyAffiliatedPages": [
		"Cohere Labs"
	],
	"companyRecentPosts": [
		{
			"text": "Four days ago, Cohere co-founder Nick Frosst warned about the dangers of subscriptions to proprietary LLMs on MTS Live:\n\n\"We released [North Mini Code] because we think this technology needs to be sovereign. It needs to be owned, and controlled, by the people who use it.\"",
			"datePublished": "2026-06-14T20:13:09.311Z",
			"url": "https://www.linkedin.com/posts/cohere-ai_nick-frosst-on-mts-live-activity-7472018313511092224-7lKZ"
		},
		{
			"text": "When you rent your artificial intelligence, you have no control, and no choice. This is why sovereignty and ownership matters. \n\nWhether it means using your own hardware, open source, or deep customization. Own your AI, own your future.",
			"datePublished": "2026-06-13T15:49:47.946Z",
			"url": "https://www.linkedin.com/posts/cohere-ai_when-you-rent-your-artificial-intelligence-activity-7471589649916887040-61hN"
		},
    ...
	]
}

LinkedIn Jobs Workflows & Integrations

Collecting 3,000+ unique jobs in one run

Each search returns at most 1,000 jobs, so use several different searches in one run:

{
  "keyword": ["Backend Developer", "Software Engineer"],
  "locations": ["Toronto, Canada", "Montreal, Canada", "Vancouver, Canada"],
  "publishedAt": "r2592000",
  "saveOnlyUniqueItems": true,
  "maxItems": 5000
}

This creates 6 searches (2 keywords × 3 locations). Set maxItems high enough for the total you want.

Avoid duplicates across runs (Make.com / n8n / Zapier)

saveOnlyUniqueItems only works inside one run. To avoid duplicates between scheduled runs:

1. Apify – Get Dataset Items
2. Filter – keep only dynamicFilterMatch = true (if you use dynamic filters)
3. Google Sheets / database – search for the row where jobId equals the job's jobId
4. If not found – add a new row

Lead generation & decision-maker enrichment

The actor provides job and company data. It does not identify or verify decision-makers and does not find contact emails. For lead generation, send these fields to your own downstream enrichment service: jobTitle, jobDescription, companyName, companyId, companyWebsite (needs enrichCompanyData: true) and, when available, posterFullName / posterProfileUrl. Finding emails requires a separate service.

Check out my other Products

Scrapers

• Indeed Job Scraper
• Glassdoor Job Scraper