LinkedIn Jobs Scraper — Hiring Graph, Company Growth & Intent

Competitors scrape job postings. This actor builds a hiring graph — track company growth, hiring intent, emerging skills, department expansion, and labor-market trends from public LinkedIn jobs. Paste companies, a role, or a skill; get a ranked hiring queue plus the hiring history behind it. Same input + fields as worldunboxer/rapid-linkedin-scraper, minus the duplicates.

The real competitor isn't another scraper. It's the spreadsheet. Every LinkedIn-jobs tool hands you rows: title, company, salary, applicant count. The question the buyer actually has — which companies are staffing up, in what, and what does it signal — still lives in a human's head after an hour in Sheets. This actor answers it in one run.

The field gives you LinkedIn job rows. This actor tells you who's hiring and what it signals.

What this actor does

• What it is: A public LinkedIn jobs scraper that returns a ranked hiring queue plus the hiring history behind each company.
• What it reads: Public LinkedIn job postings only — title, company, location, salary as listed, applicant count, seniority, function, posted time.
• What it returns: Hiring momentum scores, what each company is building, ghost-job (repost / long-open) detection, skill-demand signals, and a per-company hiring timeline you can replay.
• What it does NOT do: No member, profile, people, or candidate data. No login. No "this company is doing layoffs" or "this job is fake" verdicts. No salary inference, no procurement predictions, no outreach.
• Who it's for: Sales / GTM teams, recruiters, investors and analysts, job-seekers, and labor-market researchers.

Hiring Memory: the part nobody else keeps

Competitors show today's jobs. This actor remembers every hiring signal it sees, run after run. So for every company it has seen, you can pull:

• ✓ Hiring Replay — the full hiring story, start to now
• ✓ Company Timeline — month-by-month posting volume and top functions
• ✓ Team Expansion History — which departments grew, and when
• ✓ Repost History — which roles keep getting reposted, and for how long

Hiring Memory accumulates a timestamped record of every company's postings on every entity any run touches. That is the moat: signals get copied in a sprint, but accumulated hiring history can't be backfilled.

The README is organized around what the memory unlocks:

• Find — Hiring Queue, hiring momentum, skill demand. Who's staffing up fastest right now.
• Track — a watchlist or account feed: what changed in your tracked companies since the last run.
• Remember — Hiring Memory and Company Timeline: the hiring trajectory behind the score.
• Replay — Hiring Replay: a company's full hiring story, start to now.

In short: a LinkedIn jobs scraper that ranks companies by how fast they're hiring, explains what they're building, and keeps the hiring history so the signal compounds with every run.

Why people use it:

• Find companies hiring now — ranked by hiring momentum, not a raw row dump.
• Track competitor and account hiring — a daily feed of what changed since your last run.
• Detect team build-outs — which departments (AI, Data, Security, Sales) are expanding.
• Spot reposted and long-open "ghost" jobs.
• Read hiring as a buying signal — hiring intent data for GTM, without the enterprise contract.
• Monitor how hiring changes over time — the hiring history nobody else keeps.

What you get from one call

Input: { "mode": "companies", "companies": ["Stripe", "Datadog", "Ramp"], "rankBy": "hiringMomentum" }

Returns:

• A ranked hiring queue: these companies are staffing up fastest right now.
• Per company: a momentum score and grade, a one-line "why now," and a prioritisation action.
• What each company is building (the functions and skills behind the surge).
• The hiring history behind the score, where Hiring Memory has seen the company before.

Outcome: "These companies are staffing up fastest right now — and here's what they're building."

One company in the Hiring Queue looks like this:

{
  "company": "Datadog",
  "momentumScore": 84,
  "grade": "A",
  "companyProfileLabel": "scaling",
  "attentionPriority": "high",
  "whyNow": ["Posting volume up sharply vs baseline", "First data-platform roles detected"],
  "recommendedAction": "Add to watchlist and review this week"
}

Typical time to first result: under a minute for a small company list. Time to integrate: minutes via the Apify API.

What makes this different

Other LinkedIn scrapers	This actor
Job rows	Hiring signals
Duplicates	Deduped + reposts linked
Today's jobs	Hiring history
Raw exports	Ranked opportunities
No monitoring	Watchlist deltas + Hiring Feed

• Hiring Memory — accumulated, timestamped hiring history per company. A row scraper can't reproduce it without the accumulated runs.
• Decision layer, not rows — momentum score, what they're building, ghost-job detection, and a prioritisation action, instead of a spreadsheet you have to interpret.
• Dedup turned into signal — duplicates and reposts are removed by default, and the repost pattern becomes the ghost-job (long-open) signal instead of noise.

Detect team expansion — companies building AI, Data & Security teams

Find companies building, without manually monitoring job boards:

• AI / ML teams — first ML, MLOps, or applied-science roles appearing
• Data teams — data engineering, analytics, and platform hiring
• Cybersecurity teams — security, GRC, and detection-engineering roles
• Platform / infra teams — SRE, platform, and DevOps build-outs
• Sales teams — GTM headcount ramps

Each company carries the functions and skills behind the surge, so you see what they're building, not just that they're hiring. This is the workforce-analytics view: department-level hiring trends read straight from public postings.

Competitor hiring tracker

Name a watchlist over your competitor or ICP account list and re-run on a schedule:

{ "mode": "companies", "companies": ["Snowflake", "Databricks", "Confluent"], "watchlistName": "competitors" }

Each run reports, since your last run: new openings, new departments, hiring surges, and hiring slowdowns — a delta feed for Slack or your CRM. Hiring is a buying signal: a competitor staffing a new team is in-market for what that team needs. It is hiring intent data and labor-market intelligence without an enterprise contract.

Five scraping modes for LinkedIn jobs

One input shape, five modes. Each answers a different question.

companies — rank named companies by hiring momentum (headline mode)

{ "mode": "companies", "companies": ["Stripe", "Datadog", "Ramp"], "rankBy": "hiringMomentum" }

Outcome: these companies are staffing up fastest right now, and here's what they're building.

roles — demand for a job title

{ "mode": "roles", "roles": ["data engineer"], "location": "United States" }

Outcome: demand for this role — who's hiring, how contested, and the salary band.

market — a skill or sector trend

{ "mode": "market", "market": ["Python"], "rankBy": "skillDemand" }

Outcome: skill-demand trajectory plus the standout hirers.

watchlist — what changed since your last run

{ "mode": "companies", "companies": ["Snowflake", "Confluent", "Databricks"], "watchlistName": "my-icp-accounts" }

Outcome: what changed in your tracked companies' hiring since the last run. Re-run daily for an account-hiring feed.

urls — public LinkedIn job / search URLs

{ "mode": "urls", "urls": ["https://www.linkedin.com/jobs/search/?keywords=ml%20engineer"] }

Outcome: direct input from job or search URLs, with migration parity. Member, profile, and company-login URLs are rejected.

Who uses this LinkedIn jobs scraper

Every one of these workflows ends in a human in a spreadsheet. This actor replaces that step.

Best for sales and GTM teams

Hiring is a buying signal — a company scaling its data team is in-market for data tooling. Run a watchlist over an ICP account list and re-run daily for a "who started hiring [your buyer persona]" feed. Lead with the gtm output pack. Key outputs: momentum score, what they're building, attention priority, recommended action.

Best for recruiters and staffing

Track skill and role demand and watch competitor hiring without re-scraping and diffing by hand. Reposts are linked and deduped, so the same role doesn't show ten times. Key outputs: skill demand, ghost-job (long-open) roles, salary band, who else is hiring the same role.

Best for investors and analysts

Company growth read from hiring velocity, with the timeline behind it instead of a manual posting count. Department-level expansion (Eng, Sales, Security, Data) becomes visible. Key outputs: momentum trajectory, hiring replay, function and geo expansion.

Best for job-seekers

Tell a fresh role from a perennial repost, and see how contested a posting is. Key outputs: freshness, repost count and days listed, applicant context, salary as listed.

Best for labor-market researchers

Skill and geo demand trends with a time series you don't have to build. Key outputs: skill-demand index, demand momentum, standout hirers per skill.

Drop-in migration from existing LinkedIn scrapers

Same input + fields as worldunboxer/rapid-linkedin-scraper — minus the duplicates, plus what it signals.

Set outputProfile to compat and you get the standard LinkedIn-job field set (job_title, company_name, location, time_posted, salary_range, num_applicants, job_url and the rest), with duplicates removed. The same inputs are accepted, so an existing pipeline keeps working.

outputProfile	What you get
signals (default)	The full decision layer: job substrate + momentum, signals, deltas, dedup.
compat	The exact popular-scraper field set, duplicates removed — drop-in migration.
minimal	The essential job fields only.

Output: dataset views

The dataset ships progressive-disclosure views, ordered for a five-second read down to raw rows. Open the Hiring Queue first.

Order	View	Reads	Audience
1	Hiring Queue (default)	Priority, momentum, grade, why now, action	Every buyer
1b	Ignore Queue	Companies to deprioritise	"Don't chase these"
2	Scaling Companies	Companies with a hiring surge / high momentum	GTM, recruiters, investors
2b	Hiring Replay / Company Timeline	A company's hiring story: timeline, peak, trajectory, patterns	The moat surface — GTM, investors
3	Ghost-Job Detector	High-repost / long-open roles, surfaced descriptively	Job-seekers, recruiters
4	Skill Demand	Roles ranked by skill-demand signal	Recruiters, analysts
5	New Functions / Geo	Function and geographic expansion events	GTM, investors
6	New Since Last Run	Watchlist delta — what changed	Watchlist / GTM
7	Hiring Feed	Events by importance and severity	Slack / agent
8	Compat (dedup'd)	Standard LinkedIn-job fields, duplicates removed	Migrators
9	Coverage & Errors	How coverage was achieved, the cap workaround, truncation	Trust / debug
10	Summary	Run rollup	Exec read

Buyer controls

• intent — a one-click shortcut (find_growth, track_accounts, role_demand, ghost_check, salary_benchmark) that sets mode, ranking, and output pack for a common job.
• rankBy — order the hiring queue by hiringMomentum (default), skillDemand, freshness, salary, competition, or recency. hiringMomentum and skillDemand are what no other LinkedIn-jobs actor ships.
• outputPack — persona-tuned emphasis: gtm, recruiter, investor, jobseeker, analyst, or raw.
• alerts — emit only actionable items (minMomentumScore, onlyNewSinceLastRun, surgeMinPct, alertOnNewFunction, severity) and fire compact Hiring Cards.
• filters — job-attribute filters only: seniority, employment type, work arrangement, posted-within, salary minimum. No candidate or people filters.
• enableHiringMemory — on by default. Turn it off to keep a run out of the shared hiring history.
• limits — cap results per search, companies, cap-bypass partitions, detail fetches, and runtime.

First run tips

• Start with the canonical run — three companies, rankBy: hiringMomentum. It returns the Hiring Queue in under a minute and shows what the actor is for before you scale up.
• Set a watchlistName to unlock deltas — without it you get a snapshot. With it, re-running gives you "what changed since last run." First run on a fresh watchlist returns a first-sight baseline.
• Hiring Memory compounds with use — on the first sighting of a company, records carry a first-sight fallback and the history is thin. Depth accrues run over run, so a company you (or anyone) have run before returns more trajectory. This is expected on day one, not an error.
• Use a residential proxy — strongly recommended for the public LinkedIn endpoint; it's the default in the input form.
• Open the Hiring Queue view first — the raw dataset has every field, but the view is the five-second read.

Input examples

Find growing companies (headline):

{ "mode": "companies", "companies": ["Stripe", "Datadog", "Ramp"], "rankBy": "hiringMomentum" }

Track an account list (GTM feed):

{ "mode": "companies", "companies": ["Snowflake", "Confluent", "Databricks"], "watchlistName": "icp-data-accounts", "outputPack": "gtm", "alerts": { "minMomentumScore": 60, "onlyNewSinceLastRun": true } }

Migrate an existing pipeline (compat):

{ "mode": "roles", "roles": ["software engineer"], "location": "United States", "outputProfile": "compat" }

Output example

A Hiring Queue record:

{
  "recordType": "company",
  "company": "Datadog",
  "companyId": "datadog",
  "attentionPriority": "high",
  "momentumScore": 84,
  "grade": "A",
  "headline": "Posting volume up sharply across Eng and Data — staffing a platform team",
  "recommendedAction": "Add to watchlist and review this week",
  "riskState": "growing",
  "timestamp": "2026-06-21T09:14:00Z"
}

A Ghost-Job Detector record:

{
  "recordType": "job",
  "company": "Northstar Logistics",
  "title": "Senior Backend Engineer",
  "canonicalJobId": "northstar-logistics-senior-backend-engineer-remote",
  "location": "Remote",
  "repostCount": 4,
  "daysListed": 138,
  "timestamp": "2026-06-21T09:14:00Z"
}

Output fields

Field	Type	Description
recordType	string	summary, company, job, hiringCard, event, hiringFeed, hiringReplay, watchlistBriefing, coverage, error.
company / companyName	string	Company name (analysed entity / job row).
companyId	string	Stable company id.
title	string	Job title.
canonicalJobId	string	Stable id linking a role across reposts.
location	string	Job location.
attentionPriority	string	high, medium, low, none.
momentumScore	integer	Hiring momentum score.
grade	string	A–F momentum grade.
recommendedAction	string	Prioritisation-only next step.
riskState	string	growing, steady, softening, frozen (descriptive).
repostCount	integer	Times the role was reposted.
daysListed	integer	Days listed across observations.
eventType / eventImportance / severity	mixed	Hiring Feed event fields.
job_title / company_name / time_posted / salary_range / num_applicants / job_url	mixed	Compat fields (salary as listed).
truncated / truncatedReason	mixed	Coverage: set when a cap (runtime or the ~1000-result cap) bound the run.

Scrape LinkedIn jobs using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/linkedin-jobs-scraper").call(run_input={
    "mode": "companies",
    "companies": ["Stripe", "Datadog", "Ramp"],
    "rankBy": "hiringMomentum"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("recordType") == "company":
        print(f"{item['company']}: {item.get('momentumScore')} ({item.get('grade')}) — {item.get('headline')}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/linkedin-jobs-scraper").call({
    mode: "companies",
    companies: ["Stripe", "Datadog", "Ramp"],
    rankBy: "hiringMomentum",
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    if (item.recordType === "company") {
        console.log(`${item.company}: ${item.momentumScore} (${item.grade}) — ${item.headline}`);
    }
}

cURL

curl -X POST "https://api.apify.com/v2/acts/ryanclinton~linkedin-jobs-scraper/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "mode": "companies", "companies": ["Stripe","Datadog","Ramp"], "rankBy": "hiringMomentum" }'

curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

What this actor does NOT do

Jobs-only is the deliberate design, not a gap to apologise for. The whole actor is built on public job postings, and that boundary is what keeps it clean and defensible.

• No member, profile, people, or candidate data. Ever. That data is login-walled and personal; this actor never touches it.
• No auth circumvention. No login, no cookies, no member-only pages — the public job surface only. Where access is restricted from shared IPs, a residential proxy is used; nothing is bypassed.
• Descriptive freeze and ghost signals, never verdicts. A posting-volume drop is reported as a drop with evidence, never as "this company is doing layoffs." A high-repost, long-open role is reported with its repost count and days listed, never as "this job is fake." The Ghost-Job Detector is a name for a view; the data never accuses a specific job.
• Salary as listed, never inferred. If a posting omits salary, the actor leaves it out — it does not guess a band.
• No procurement or purchase predictions. "What a company is building" is read from its public postings. It never claims a company will buy a specific vendor.
• No outreach execution and no contact data. Recommended actions are prioritisation only (review, track, compare, re-check). There is no contact, email, or call step.

Limitations

• History is thin on first sighting. Hiring Memory compounds with use. A company seen for the first time returns a first-sight baseline; depth accrues run over run.
• Salary is sparse. Many LinkedIn postings omit salary; salary signals only appear when disclosed, and data-quality confidence reflects this.
• Public job surface only. Company depth that lives behind login is out of reach by design.
• LinkedIn caps a single search. A query is partitioned to extend coverage, and the Coverage view reports exactly how, but extreme result counts can still be bound — the run reports truncation rather than failing silently.
• The public endpoint can tighten. A browser fetch path is available as a fallback; the Coverage view surfaces any shortfall.

Integrations

• Zapier — push the Hiring Feed into Slack or a CRM when a company crosses a momentum threshold.
• Make — schedule a daily watchlist run and route new surges to your team.
• Google Sheets — drop the Hiring Queue into a shared sheet for non-technical reviewers.
• Apify API — trigger runs and read the dataset from any HTTP client.
• Webhooks — fire on run completion to a downstream service.
• LangChain / LlamaIndex — feed hiring signals to an agent that decides who to act on.

Combine with other Apify actors

Actor	How to combine
Website Contact Scraper	Take the scaling companies, find their site contacts for outreach.
Email Pattern Finder	Resolve the email convention for a high-momentum account.
HubSpot Lead Pusher	Push the GTM hiring feed into your CRM as buying signals.
Company Deep Research	Pull a full company report on a top scaler before you act.
Bulk Email Verifier	Verify the contacts you sourced from scaling companies.

Responsible use

• This actor reads publicly available job-posting data from LinkedIn's public job surface. It does not log in, bypass authentication, or access member, profile, or company-login pages.
• Hiring history retains public job-posting metrics only (company, title, counts, salary as listed) on a capped rolling window. It stores no candidate or applicant personal data — applicant count is a number, not people.
• Freeze and ghost signals are descriptive, evidence-backed observations about posting patterns, never verdicts about a company's health or a job's legitimacy.
• Users are responsible for ensuring their use complies with applicable laws and platform terms, including data-protection rules in their jurisdiction.
• For guidance on web scraping legality, see Apify's guide.

FAQ

What is the difference between this and a regular LinkedIn jobs scraper? A regular scraper returns job rows and leaves the interpretation to you. This actor returns the same rows (deduped) plus a hiring queue: who's scaling, what they're building, and which roles are reposted ghosts — and it remembers the history so the signal compounds.

How does Hiring Memory work, and what do I get on the first run? It accumulates a timestamped record of every company's postings on every entity a run touches. On a company's first sighting, records carry a first-sight fallback and history is thin. Re-run, or run a company that's been seen before, and you get more trajectory. Depth accrues with use; it is not backfillable by a clone.

Can I use this for sales and buying-signal data? Yes. Hiring is a buying signal — a company scaling a team is in-market for what that team needs. Run a watchlist over your ICP accounts with the gtm pack and re-run daily for a "who started hiring [persona]" feed. It is a practical alternative to expensive hiring-intent platforms for teams that want the signal without the contract.

Does it tell me a company is doing layoffs or that a job is fake? No. A posting-volume drop is reported descriptively with evidence, and a long-open or reposted role is reported with its repost count and days listed. The actor never asserts layoffs or a fake job — those are verdicts it deliberately does not make.

Can I migrate from worldunboxer/rapid-linkedin-scraper without changing my pipeline? Yes. Set outputProfile to compat for the same field set, duplicates removed. The same inputs are accepted, so your existing run keeps working — you just gain the dedup and, optionally, the signal layer.

Does it scrape LinkedIn member or profile data? No. It is jobs-only by design: public job postings, no login, no member, profile, people, or candidate data. That is a permanent boundary, not a setting.

How do I find which companies are hiring for a specific skill? Use market mode with the skill, ranked by skillDemand. You get a skill-demand trajectory plus the standout hirers, drawn from the accumulated skill history.

What does ranking by hiring momentum mean? Companies are ordered by how fast their public hiring is moving relative to their own baseline, weighted by how many functions and how senior the hiring is. The score is auditable — each company carries the reasons behind it.

How do I track changes in my accounts over time? Set a watchlistName and re-run on a schedule. Each run reports what changed since the last one — new surges, new functions, frozen hiring — as a delta feed.

Can I get only the high-priority companies? Yes. Use the alerts control with minMomentumScore (and onlyNewSinceLastRun with a watchlist) to emit only the companies worth acting on, plus compact Hiring Cards for automation.

Does it handle duplicate and reposted jobs? Yes, by default. Reposts are linked and duplicates removed, and the repost pattern itself becomes the ghost-job (long-open) signal rather than clutter.

Is it legal to scrape public LinkedIn job postings? This actor reads public, no-login job-posting data and never touches member or profile data or authentication. Legality depends on your jurisdiction and intended use; consult legal counsel for your situation, and see Apify's guide.

Input parameters reference

Parameter	Type	Required	Default	Description
mode	string	Yes	companies	companies, roles, market, watchlist, or urls.
companies	string[]	For companies/watchlist	["Stripe","Datadog","Ramp"]	Company names to analyse.
roles	string[]	For roles	[]	Job titles or skills to analyse.
market	string[]	For market	[]	Skills or sectors to track.
urls	string[]	For urls	[]	Public LinkedIn job-view or job-search URLs.
location	string	No	""	Optional location filter applied to all searches.
watchlistName	string	No	""	Name a watchlist to persist history and get deltas.
intent	string	No	""	One-click shortcut that sets mode, ranking, and pack.
rankBy	string	No	hiringMomentum	How to rank the hiring queue.
outputProfile	string	No	signals	signals, compat, or minimal.
outputPack	string	No	gtm	Persona emphasis for the queue.
enableHiringMemory	boolean	No	true	Keep an accumulating hiring history.
alerts	object	No	{}	Emit only actionable items and fire Hiring Cards.
filters	object	No	{}	Job-attribute filters only.
limits	object	No	{}	Result, company, partition, fetch, and runtime caps.
forceBrowser	boolean	No	false	Use the browser path if the default is restricted.
proxyConfiguration	object	No	residential	Proxy configuration.

Help us improve

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1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.