Public job postingsA job posting tells you something about what a company wants to build. Track the roles it's advertising and use the descriptions to research potential expansion or new technical priorities.
Try these examples for marketing, growth and competitor research.
If you're a data consultancy, look for companies hiring analytics engineers in London. Read the descriptions for relevant tools, then research whether the company might need project support.
Keep a shortlist with the job link, the date you saw it and a reason to research the account.
Watch public openings in sales, partnerships and engineering. Compare new postings over time to investigate changes in priorities or possible market expansion.
Keep a hiring timeline with your research notes.
For a training business, track postings that mention Python, SQL or dbt in the same region. Review descriptions and seniority before planning course content.
Base the skill-demand study on the postings you collected and state the sample coverage.
FROM FIRST RUN TO REPEATABLE WORKFLOW
{
"keywords": ["python developer"],
"locations": ["London"],
"datePosted": "week",
"maxItems": 20,
"scrapeJobDetails": false
}Paste this into the Actor’s JSON input editor. Replace the example targets with yours before running.
Check the current input form on ApifyYou can build a search from keywords and locations or copy a filtered LinkedIn Jobs URL. Start with listing cards, then turn on job details when you need descriptions for skills research.
| Search method | When to use it | What changes |
|---|---|---|
| Keywords and locations | When to use itYou want to find relevant results before choosing specific targets. | What changesJob titles or skills. Every keyword is paired with every location: three keywords and two locations create six searches. Overlapping jobs are delivered only once. |
| Search page URLs | When to use itYou already configured the search on the source website. | What changesPaste filtered LinkedIn job-search URLs. They preserve the site’s search filters. Company pages become name searches; individual job-detail URLs are not supported here. Clear prefilled keywords if you only want the pasted searches. |
| Company IDs | When to use itYou know the exact entity you want to collect. | What changesRestrict to numeric LinkedIn company IDs from the search URL’s f_C filter. A company’s page slug is not the same identifier. |
Each page contains 10 jobs. The public search has a ceiling around 1,000 results per search; raising page limits cannot remove it. Split large searches by location, date or job type.
Compare the related scraperUse the exact field names below in JSON. In Apify’s form, enter list items separately, choose filters, and keep numbers and booleans in their proper types.
Default and prefill are different. A default applies when you omit a setting; a prefill is an example already entered in Apify’s form. Review prefilled targets and limits before every run. Some settings have no schema default. You still need to supply at least one supported target.
searchUrlsPaste filtered LinkedIn job-search URLs. They preserve the site’s search filters. Company pages become name searches; individual job-detail URLs are not supported here. Clear prefilled keywords if you only want the pasted searches.
keywordsJob titles or skills. Every keyword is paired with every location: three keywords and two locations create six searches. Overlapping jobs are delivered only once.
locationsCity, region or country names recognized by LinkedIn. Each location adds searches, not a union within one search. Use geoId when a name could refer to several places.
geoIdLinkedIn’s numeric location identifier, copied from geoId in a filtered search URL. It selects a specific place more reliably than a text name.
companyIdsRestrict to numeric LinkedIn company IDs from the search URL’s f_C filter. A company’s page slug is not the same identifier.
datePostedany, month, week or day. day means the past 24 hours, not a calendar date. For daily monitoring, combine day with sortBy date and retain job IDs between runs.
any month week daysortByrelevance uses LinkedIn’s ranking; date returns the newest postings first. With a global result cap, sort order determines which jobs you collect before stopping.
relevance dateexperienceLevelA list of seniority levels. Several selections broaden matching levels. Use the suggested spellings; unfamiliar values are skipped with a note rather than becoming a new level.
internship entry associate mid-senior director executivejobTypeEmployment types such as full-time, contract or internship. These are job-search filters, distinct from workplaceType, which describes remote, hybrid or on-site work.
full-time part-time contract temporary volunteer internship otherworkplaceTypeSelect on-site, remote, hybrid, or several as a list. A remote filter is not a promise that the employer accepts applicants from every country.
on-site remote hybriddistanceSearch radius in miles, from 0 to 100. Relevant to location-based searches; it does not change whether a role is classified as remote.
scrapeJobDetailsAdds descriptions, seniority, employment type, functions, industries and applicant counts with one extra request per job. Turn off for a fast hiring watchlist; use a proxy when on.
maxItemsHard cap on unique jobs across all keywords, locations and pasted searches. It is not per search. Empty removes the cap; maxPagesPerSearch still limits each search.
maxPagesPerSearchEach page contains 10 jobs. The public search has a ceiling around 1,000 results per search; raising page limits cannot remove it. Split large searches by location, date or job type.
maxConcurrency1-10 independent searches at once, default 2. More workers can increase throttling. Keep it modest unless you have enough proxy exits for the workload.
delayMsPause between page requests, in milliseconds. Increase it when the source throttles you; reducing it does not remove network delays or the source’s rate limits.
proxyConfigurationStrongly recommended for search and especially full descriptions. LinkedIn can stop serving details before it stops serving search cards, so partial descriptions are not evidence that the postings lack them.
proxyRotationsRetries refused work with a new proxy session. More retries can recover temporary blocks but add time and traffic. Keep the default until the log shows a reason to change it.
resumeSaves progress about every 30 seconds so an Apify restart or migration can continue the current run. Leave it on for normal use.
continueFromLastRunContinues unfinished work from the previous run with matching input. Earlier results remain in that run’s dataset. Keep false for a fresh collection or a recurring snapshot.
impersonateThe browser identity used for requests. Keep the Actor’s default unless you are diagnosing immediate blocks. It changes the request fingerprint, not the data you ask for.
This reference follows the Actor’s published input fields. Check the live form before changing a production workflow. Check the current input form on Apify.
Each example is a separate run. Start small, inspect the results, then increase coverage. Update the targets, countries and dates to match your question.
Collect recently posted data-engineering roles in London without fetching descriptions. Schedule daily and keep job IDs plus collection dates to distinguish newly found roles from jobs seen yesterday.
{
"keywords": ["data engineer"],
"locations": ["London"],
"datePosted": "day",
"sortBy": "date",
"maxItems": 50,
"scrapeJobDetails": false
}Collect full-time hybrid jobs mentioning dbt or BigQuery, with descriptions enabled. Extract repeated skill terms in SQL or ask Claude to group them. The 50-job cap covers both keyword searches together.
{
"keywords": ["dbt", "BigQuery"],
"locations": ["London"],
"datePosted": "week",
"jobType": ["full-time"],
"workplaceType": ["hybrid"],
"maxItems": 50,
"scrapeJobDetails": true,
"maxConcurrency": 2
}Look at entry and associate Python roles advertised as remote in Germany. Remote does not mean worldwide hiring eligibility. Read the description for residence, time-zone and work-authorization requirements.
{
"keywords": ["python developer"],
"locations": ["Germany"],
"datePosted": "week",
"experienceLevel": ["entry", "associate"],
"workplaceType": ["remote"],
"maxItems": 50,
"scrapeJobDetails": true
}Keep job IDs, company, location, posting time and source links. Detailed descriptions let you extract skills, seniority and employment terms, but salaries and applicant fields can be absent. This is a public job dataset, not recruiter contact data. For hiring trends, deduplicate within a snapshot and keep repeated observations over time.
resume protects the current run if Apify restarts it. continueFromLastRun continues an earlier run with the same configuration; earlier records stay in the earlier dataset. Combine both datasets for the complete collection, and raise a previously reached result cap when continuing. Start fresh when you want to see what changed today.
Save one configuration above as input.json. Set APIFY_TOKEN to your Apify API token in your terminal, then send the file as the request body.
curl --fail-with-body --request POST \
--url "https://api.apify.com/v2/actors/jmlp~linkedin-jobs-scraper/runs" \
--header "Authorization: Bearer $APIFY_TOKEN" \
--header "Content-Type: application/json" \
--data-binary @input.jsonThe response contains a run ID and defaultDatasetId, not finished results. Wait for the run to succeed, set DATASET_ID to that dataset ID, then fetch its items. For large datasets, use limit and offset to page through the export.
curl --fail-with-body \
--url "https://api.apify.com/v2/datasets/$DATASET_ID/items?format=json" \
--header "Authorization: Bearer $APIFY_TOKEN"Change one setting at a time, keep a small cap, and check the run summary before scaling up.
Adds descriptions, seniority, employment type, functions, industries and applicant counts with one extra request per job. Turn off for a fast hiring watchlist; use a proxy when on.
Strongly recommended for search and especially full descriptions. LinkedIn can stop serving details before it stops serving search cards, so partial descriptions are not evidence that the postings lack them.
LinkedIn’s numeric location identifier, copied from geoId in a filtered search URL. It selects a specific place more reliably than a text name.
Success means the Actor finished handling the request, not that the source returned data. Check SUMMARY.inputProblem, SUMMARY.problem and the log for missing targets, unsupported filters or refused requests. Test one known target with fewer filters.
Check the global limit, per-search or per-page limits, platform coverage and deduplication. Several searches can find the same record. A source’s headline count can include records the public endpoint does not return. Review unfinished jobs before treating the dataset as complete.
Keep company, role, posted_date and job_url in an account research sheet. Add your reviewed signal category and a separate column for what your team does next.
Read the setupAsk Claude to find explicit skill and tooling mentions in descriptions and cite the job URLs. Have it label any suggestion of commercial demand as a hypothesis.
Read the setupChart new posting IDs by company, location and role category. Use the same search criteria over time and show what your sample covers.
Read the setupKeep raw observations and a current job table keyed by job_id. Verify company identities before joining jobs to CRM accounts for account research.
Read the setup
Check jmlp/linkedin-jobs-scraper, then collect up to 50 analytics engineer postings in London from the past week with details. Identify explicit Python, SQL or dbt mentions, cite each job_url, and build an account research shortlist. Label service-demand interpretations as hypotheses.Keep the collection time and original IDs with your records. You’ll need them to check where a result came from or compare it with a later run.
A vacancy shows hiring intent. It doesn't confirm a completed hire, a budget or expansion. Salary and applicant counts may be missing. Check for reposts and duplicate roles before drawing conclusions about growth, and keep the job IDs and observation dates.
No. This scraper uses publicly accessible job postings and does not require your LinkedIn login or session cookie. An Apify account is needed to run it.
No. Public vacancies can suggest priorities, but they do not establish completed hires or company growth. Use them as a prompt for further research.
Source and current product details: JMLP’s LinkedIn Jobs Actor on Apify.
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