LinkedIn Jobs Scraper

Scrape LinkedIn job listings without a login, a cookie or a browser. Give it a few search terms and locations and it returns structured job rows: title, company, company page, logo, location, posting date, and — optionally — salary range, seniority, employment type, industries, applicant count and the full job description.

It reads LinkedIn's own logged-out job endpoints over plain HTTP. No account is used, nothing is authenticated, and no personal profile data is touched.

What you get

Every row is one job. Fill rates below are measured on a real Apify platform run (100 jobs, queries software engineer and registered nurse, United States, residential proxy).

Always present — from the search results

Field	Type	Fill	Notes
jobId	string	100%	LinkedIn's numeric job posting id
url	string	100%	Canonical https://www.linkedin.com/jobs/view/<jobId>
title	string	100%
company	string	100%	Falls back to the plain-text name when the company has no LinkedIn page
companyUrl	string | null	100%	null when the company has no LinkedIn page — not fabricated
companyLogo	string | null	100%	media.licdn.com URL. Grey placeholder images are never emitted
location	string	100%	As LinkedIn writes it: New York, NY, Georgia, United States, Greater Kolkata Area
postedTimeText	string	100%	Relative, as shown: 3 days ago, 15 hours ago
postedAt	string | null	100%	Date only, YYYY-MM-DD. See limitations
isRemote	boolean	100%	Inferred from the job title. See limitations
isNewBadge	boolean	100%	LinkedIn's "new posting" styling on the date
benefitBadge	string | null	70-77%	Raw badge text: Actively Hiring, Be an early applicant, Medical insurance +3 benefits
searchQuery	string	100%	The query that produced this row
searchLocation	string	100%	The location that produced this row
scrapedAt	string	100%	ISO timestamp
detailFetched	boolean	100%	Whether the fields below were actually read

Only with Fetch job details enabled

One extra request per job. Fill rates from the same platform run.

Field	Type	Fill	Notes
seniorityLevel	string | null	100%	About 90% of real jobs say Not Applicable
employmentType	string | null	100%	Full-time, Part-time, Internship, Contract, Volunteer, Other
jobFunction	string | null	100%	Single joined string, e.g. Engineering and Information Technology
industries	string | null	100%	Single joined string, e.g. Financial Services. See limitations
applicantCountText	string | null	72%	A bucket, not a number. See limitations
salaryText	string | null	20%	Raw as displayed, e.g. $123,500.00/yr - $150,000.00/yr. Most jobs publish none
easyApply	boolean | null	100%	null when the page carries no apply marker at all
description	string | null	100%	Full text, ~3,900 characters on average

Without Fetch job details these nine fields are null and detailFetched is false.

Example row

{
  "jobId": "4453687841",
  "url": "https://www.linkedin.com/jobs/view/4453687841",
  "title": "Software Engineer II (Java)",
  "company": "JPMorganChase",
  "companyUrl": "https://www.linkedin.com/company/jpmorganchase",
  "companyLogo": "https://media.licdn.com/dms/image/v2/D4E0BAQGxpntCyRgsuA/company-logo_100_100/...",
  "location": "New York, NY",
  "postedTimeText": "3 days ago",
  "postedAt": "2026-08-13",
  "isNewBadge": false,
  "benefitBadge": "Actively Hiring",
  "isRemote": false,
  "seniorityLevel": "Not Applicable",
  "employmentType": "Full-time",
  "jobFunction": "Engineering and Information Technology",
  "industries": "Financial Services",
  "applicantCountText": "Over 200 applicants",
  "salaryText": "$123,500.00/yr - $150,000.00/yr",
  "easyApply": false,
  "description": "Job Description You're ready to gain the skills and experience needed to grow …",
  "detailFetched": true,
  "searchQuery": "software engineer",
  "searchLocation": "United States",
  "scrapedAt": "2026-08-17T04:49:15.076Z"
}

Input

Input	Type	Default	What it does
searchQueries	string[]	required	Job titles or keywords. Each is run against every location
locations	string[]	["United States"]	Free-text places LinkedIn geocodes, e.g. Texas, United States, London, England, United Kingdom
maxResultsPerQuery	integer	100	Jobs kept per query/location pair. Capped at 1000 (LinkedIn's own ceiling)
remoteOnly	boolean	false	Keep only remote jobs. Title-based — see limitations
maxAgeDays	integer	0	Only jobs posted within N days. 0 = any date. Genuine server-side filter
easyApplyOnly	boolean	false	Only jobs you apply to without leaving LinkedIn. Genuine server-side filter
fetchJobDetails	boolean	false	Fetch each job's page for the nine extra fields. One extra request per job, billed separately
experienceLevel	select	any	Seniority filter. Requires fetchJobDetails
jobType	select	any	Employment type filter. Requires fetchJobDetails
maxItems	integer	0	Hard cap across the whole run. 0 = no cap
proxyConfiguration	object	Apify residential	LinkedIn blocks datacenter IPs. Leave as is

Minimal input

{
  "searchQueries": ["software engineer"],
  "locations": ["United States"],
  "maxResultsPerQuery": 100
}

Everything on

{
  "searchQueries": ["product manager", "data analyst"],
  "locations": ["United States", "Canada"],
  "maxResultsPerQuery": 200,
  "maxAgeDays": 7,
  "easyApplyOnly": true,
  "fetchJobDetails": true,
  "maxItems": 500
}

Use cases

• Job boards and aggregators — pull fresh listings for a niche daily with maxAgeDays: 1.
• Recruiting intelligence — track which companies are hiring for which roles, and where.
• Salary research — salaryText gives real posted ranges for the ~20% of jobs that publish one.
• Lead generation — a company hiring 15 sales reps is a company with a budget.
• Job-seeker automation — easyApplyOnly plus maxAgeDays: 1 produces a short, actionable list.
• Labour-market analysis — posting volume and postedAt over time, by title and geography.

Limitations — read this before you rely on a field

This Actor reads LinkedIn's public, logged-out surface. That surface is narrower than the logged-in site, and several things people expect simply are not published there.

isRemote is inferred from the job title, not reported by LinkedIn. LinkedIn's logged-out pages contain no workplace-type field anywhere — not on the search card, not on the job page. Its f_WT remote parameter is accepted and then silently ignored (a made-up parameter returns byte-identical results). Matching the description text was tested and rejected: every match in a 40-job sample was a false positive, such as "Fridays are remote-friendly ... we prioritize in-person and hybrid work". So remoteOnly does two things instead: it appends remote to the keywords sent to LinkedIn, which genuinely changes what is returned, and then keeps only jobs whose title advertises remote work. On a platform test this produced 25 rows all titled remote (Backend Engineer (Remote Position), Software Engineer (Fully Remote)) against 0 remote-titled rows out of 50 in the unfiltered control. It is precise but it is not exhaustive — a remote job whose title does not say so will be missed.

postedAt is a date, never a time. LinkedIn publishes 2026-08-13 and nothing finer anywhere in the markup. If you need "15 hours ago" granularity, that lives in postedTimeText. postedAt comes from the search card only, so it is present on every row regardless of fetchJobDetails.

applicantCountText is a bucket, not a count. LinkedIn only ever renders Be among the first 25 applicants or Over 200 applicants. It is kept as the raw display string on purpose. Do not parse it into a number — those are thresholds, not measurements. About 28% of job pages show no applicant element at all.

industries and jobFunction are single strings, not arrays. LinkedIn joins multiple values with " and ", and the individual values themselves contain " and " (Hospitals and Health Care, Computer and Network Security and Software Development). Splitting would be lossy and ambiguous, so the raw string is emitted unchanged.

seniorityLevel is mostly uninformative. About 90% of real listings report Not Applicable. Filtering on experienceLevel therefore discards most results.

experienceLevel and jobType are client-side filters and can be slow and expensive. LinkedIn's f_E and f_JT parameters are accepted by its public search and then ignored, so this Actor reads the real values from each job page instead. That means every scanned card costs a request whether or not it matches, and the run keeps scanning until it fills your quota or reaches LinkedIn's 1000-result ceiling. A measured worst case: jobType: "part-time" on software engineer scanned all 1000 reachable results, found 1 match, and took 11 minutes. Both inputs refuse to run without fetchJobDetails rather than silently returning unfiltered results.

easyApply is derived from the apply button's tracking attribute, cross-checked against LinkedIn's own Easy Apply filter (24 of 25 rows returned by that filter were detected as Easy Apply, against 4 of 50 in the unfiltered control). The visible button text is "Apply" in both cases, so it cannot be read from the label. A job page carrying neither marker yields null rather than a misleading false.

1000 results per query and location, hard. LinkedIn refuses any result offset at or beyond 1000. To collect more, split the search into narrower queries or more specific locations.

Results are deduplicated by jobId across the whole run. LinkedIn's result window drifts between requests, so consecutive pages genuinely overlap. A job matching two of your queries is emitted once, attributed to whichever query reached it first.

benefitBadge is a multi-purpose badge. It is not an Easy Apply flag and not an applicant count. Only the raw string is emitted; no booleans are derived from it.

Residential proxy is required. LinkedIn blocks Apify's datacenter IPs. The default configuration is correct — changing it will produce empty runs.

Pricing

Pay per event:

Event	Price	When
Actor start	$0.0005	Once per run
Job scraped	$0.0004	Per job that reaches the dataset
Job details	$0.0008	Per job whose detail page was successfully read

Blocked requests, empty pages and jobs discarded by a filter are never charged — you pay for rows you actually receive. 1,000 jobs without details is $0.40; with full details it is $1.20.