LinkedIn Hiring Signals — Company Hiring Intent & Velocity

Point this Actor at a list of companies and learn who is hiring, for what, and how fast — a company-qualified sales-intent feed built from public LinkedIn job postings (no login, no cookies). Rivals hand you a flat job search; this resolves the employer entity, computes hiring velocity, and fuses the company's firmographics into one clean record — every field provenance-tagged.

• 🎯 Company-centric, not a job dump — one record per company: open-role count, momentum, and the full role breakdown.
• 📈 Velocity, not a snapshot — 7 / 30 / 90-day posting deltas show who's accelerating hiring, the strongest growth/buying signal.
• 🏢 Firmographics fused inline — employee count, industry, HQ, domain & followers, so "who's hiring" becomes "500-person fintechs that added 12 sales roles this month".
• 🔗 Entity-resolved & joinable — each company resolved to its LinkedIn id + domain, so records join cleanly to your CRM. No login, no cookies.

Quick start (API)

Track three companies and get their hiring-signal records in one call:

curl -X POST "https://api.apify.com/v2/acts/foxlabs~linkedin-hiring-signals/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "companies": ["stripe", "datadog", "https://www.linkedin.com/company/openai/"], "maxJobsPerCompany": 200 }'

Prefer no code? Open the Input tab, paste your companies (URLs or slugs), and click Start — then download the results.

What you get

One clean, flat record per company:

Field	Type	Description
companyName	string	Company name (from the LinkedIn page; falls back to the slug)
companySlug	string	LinkedIn company handle from /company/{slug}
companyLinkedInUrl	string	Canonical company page URL
companyId	string | null	Numeric LinkedIn organization id (urn:li:organization:…)
companyDomain	string | null	Company website domain — the clean join key for CRM / enrichment
employeeCount	number | null	Employee count (firmographic)
industry	string | null	Industry (firmographic)
hq	string | null	Headquarters — city, region, country
followers	number | null	LinkedIn follower count
website	string | null	Company website URL
founded	number | null	Founding year, read from the company's public About block (null when LinkedIn doesn't show one)
ownership	object	Ultimate beneficial owner (KYB), resolved from GLEIF + Companies House PSC: { registeredEntity, companyNumber, jurisdiction, ultimateBeneficialOwner, ultimateOwnerType, ultimateOwnerCountry, crossBorderOwnership, ownershipChain, ownershipConfidence, ownershipSource }. Present only above the confidence gate — never guessed
hiringSignalsCollected	boolean	false when the company id couldn't be resolved — the job feed was never queried, so no hiring field below is present (an absence of measurement, not "not hiring")
openJobCount	number	Open public roles found (up to maxJobsPerCompany)
isHiring	boolean	true when any open role was found
hiringMomentum	string	high / medium / low / not-hiring, or unknown (capped) when the cap truncated the sample (thresholds & rule in FAQ)
momentumMeasurable	boolean	false when the cap makes momentum unknowable — don't score on hiringMomentum in that case
postedLast7d / postedLast30d / postedLast90d	number	Roles posted within the last 7 / 30 / 90 days (velocity)
newestJobDate / oldestJobDate	string | null	Date range of the sampled postings (YYYY-MM-DD)
topDepartments	array	[{ name, count }] — department mix inferred from job titles
topLocations	array	[{ name, count }] — where the open roles are
openJobCountIsCapped	boolean	Present & true only when the sample hit maxJobsPerCompany. Then openJobCount and postedLast7d/30d/90d are lower bounds, oldestJobDate / topDepartments / topLocations describe the sample only
jobs	array	Full job list [{ jobId, jobUrl, title, location, postedDate }] (when Include jobs is on)
sources	object	Provenance — { hiringSignals, firmographics }
note	string	Present only when the company id can't be resolved (firmographics-only record)
source / scrapedAt	string	"LinkedIn (public)" + ISO scrape timestamp

Sample output

Illustrative record (values shown for shape, not a live pull):

{
  "companyName": "Stripe",
  "companySlug": "stripe",
  "companyLinkedInUrl": "https://www.linkedin.com/company/stripe",
  "companyId": "3641377",
  "companyDomain": "stripe.com",
  "employeeCount": 8000,
  "industry": "Software Development",
  "hq": "South San Francisco, California, US",
  "followers": 1284003,
  "website": "https://stripe.com",
  "founded": 2010,
  "hiringSignalsCollected": true,
  "openJobCount": 143,
  "isHiring": true,
  "hiringMomentum": "high",
  "momentumMeasurable": true,
  "postedLast7d": 12,
  "postedLast30d": 47,
  "postedLast90d": 118,
  "topDepartments": [
    { "name": "Engineering", "count": 58 },
    { "name": "Sales", "count": 22 },
    { "name": "Product", "count": 14 }
  ],
  "topLocations": [
    { "name": "New York, NY", "count": 31 },
    { "name": "Dublin, Ireland", "count": 18 }
  ],
  "newestJobDate": "2026-07-04",
  "oldestJobDate": "2026-03-20",
  "jobs": [
    {
      "jobId": "3901234567",
      "jobUrl": "https://www.linkedin.com/jobs/view/3901234567",
      "title": "Senior Software Engineer, Payments",
      "location": "New York, NY",
      "postedDate": "2026-07-04"
    }
  ],
  "sources": {
    "hiringSignals": "LinkedIn public jobs (f_C)",
    "firmographics": "LinkedIn company page"
  },
  "source": "LinkedIn (public)",
  "scrapedAt": "2026-07-05T09:12:44.001Z"
}

Input & filters

• Companies — the list to track, as LinkedIn company URLs or bare slugs (stripe, https://www.linkedin.com/company/datadog/). One record is returned per company.
• Max jobs per company — cap on how many open roles to sample per company for the signal computation (1–1000). Raise it for an exact openJobCount on large employers; lower it for faster, lighter runs. A low cap costs you the velocity signal: when the sample is truncated, counts become lower bounds and hiringMomentum returns unknown (capped) rather than a number that would really be measuring your cap. Keep it at the default 200 (or higher) if you rank accounts by momentum.
• Include full job list — attach the jobs[] array (title, location, posted date, URL). Turn off for compact signals-only output.
• Enrich with firmographics — add employee count, industry, HQ, domain & followers from the company's public page. On by default — it's what turns a job list into qualified sales-intent (and it reuses the page already fetched to resolve the entity, so it adds no extra fetch).
• Proxy — Residential proxy is the default and strongly recommended; LinkedIn rate-limits and blocks un-proxied and datacenter traffic.

Example inputs (copy & paste)

// 1) Target-account watchlist for sales intent (full job list, slug + URL mix)
{ "companies": ["stripe", "datadog", "https://www.linkedin.com/company/openai/"], "maxJobsPerCompany": 300 }

// 2) Compact, fast signals-only — no job array, lighter cap
{ "companies": ["shopify", "gitlab", "snowflake"], "includeJobs": false, "maxJobsPerCompany": 100 }

// 3) Deep pull with firmographics + every open role
{ "companies": ["notion", "figma"], "includeJobs": true, "enrichCompany": true, "maxJobsPerCompany": 500 }

// 4) Momentum only — no firmographics, no job array (fastest scan)
{ "companies": ["airbnb", "uber", "doordash"], "includeJobs": false, "enrichCompany": false, "maxJobsPerCompany": 150 }

// 5) Single-account deep monitor via company URL
{ "companies": ["https://www.linkedin.com/company/anthropic/"], "maxJobsPerCompany": 1000 }

// 6) Portfolio / territory screen — many accounts, enriched for scoring
{ "companies": ["plaid", "brex", "ramp", "mercury", "deel", "rippling"], "enrichCompany": true, "maxJobsPerCompany": 200 }

Use cases

• Sales intent / buying signals. Feed your target-account list → surface which accounts are ramping the team you sell to. hiringMomentum: "high" plus a jump in postedLast30d for Sales or Engineering is a timing trigger your reps can act on today — not a stale firmographic.
• Competitive & market intelligence. Track rivals over scheduled runs → watch topDepartments and topLocations shift to see where a competitor is investing (new GTM motion, a new engineering hub, a market entry) before it's public.
• Recruiting & talent-market mapping. See which companies are hiring for a function and where demand is concentrated, to time outreach and benchmark your own req load.
• CRM enrichment & scoring. Already have accounts? Match on companyDomain → append employee count, industry, HQ and a live hiring-momentum score. Missing values return null, never a guess.
• Investment / portfolio monitoring. Screen a sector or a portfolio for who's scaling: rank by postedLast30d / postedLast90d and momentum to spot acceleration and stalls between reporting cycles.
• Territory & account prioritisation. Rank a whole book of accounts by hiring velocity so SDRs work the companies actually growing, not a random alphabetical list.

Performance & throughput

Each company costs one company-page fetch (which also powers firmographic enrichment — no second request) plus a paginated pull of its public job postings. Companies are processed 6 at a time; within a company, job pages are fetched sequentially (~250 ms apart) up to your maxJobsPerCompany cap, and LinkedIn exposes roughly the first 1,000 public roles per company. Runtime therefore scales with (number of companies) × (jobs sampled per company) — so for faster, lighter runs, lower maxJobsPerCompany or turn Include jobs off. There are no external API keys; throughput is bounded by LinkedIn's public rate limits and your proxy/Apify plan.

Integrations

JavaScript (apify-client):

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('foxlabs/linkedin-hiring-signals').call({
  companies: ['stripe', 'datadog', 'openai'], maxJobsPerCompany: 200,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

Python (apify-client):

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("foxlabs/linkedin-hiring-signals").call(run_input={
    "companies": ["stripe", "datadog", "openai"], "maxJobsPerCompany": 200,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["companyName"], item["openJobCount"], item["hiringMomentum"])

Also works with Make / n8n / Zapier (Apify app → run this Actor, map the input), scheduled runs to build a hiring-trend time series, webhooks, and the Apify MCP server so AI agents can pull hiring signals on demand.

Data quality

• Public source only. Firmographics come from the company's public LinkedIn page (its structured ld+json, meta tags and About block) and hiring signals from public job postings — nothing login-gated is used. Fields LinkedIn doesn't publish for a company (e.g. followers, founded, sometimes industry) return null; values are never fabricated or guessed.
• Entity-resolved. Each company is matched to its numeric LinkedIn id and companyDomain, so records join on domain rather than fuzzy name-matching.
• Velocity is measured, not estimated. postedLast7d/30d/90d are counted from each posting's own datetime, within the sampled set (maxJobsPerCompany).
• What wasn't measured is never reported as a negative. If the cap truncated the sample, momentum returns unknown (capped) instead of a confident low; if the company id never resolved, the hiring fields are absent (hiringSignalsCollected: false) instead of 0 / not-hiring. You can tell "we looked and found nothing" from "we couldn't look".
• Departments are inferred. topDepartments is derived from job-title keywords — a strong approximation, not LinkedIn's internal taxonomy.

Pricing

Pay per company — you're billed per company record returned. Firmographic enrichment reuses the single company-page fetch already needed to resolve the entity, so it adds signal without adding cost. There's an Apify free tier to evaluate the full output before you scale, and no third-party API or proxy fees beyond your Apify plan.

FAQ

What exactly is a "hiring signal"? For each company you get its current open-role count, whether it's hiring, a momentum label, 7/30/90-day posting velocity, and a department & location breakdown — computed from its public LinkedIn job postings and paired with firmographics.

How is hiringMomentum computed? Transparently, from posting counts: not-hiring = 0 open roles; high = 5+ posted in the last 7 days or 15+ in the last 30; medium = 5+ in the last 30 (and not high); low = at least one open role but below the medium threshold.

When is hiringMomentum unknown (capped)? When the sample hit maxJobsPerCompany, so the true counts are higher than what was counted. The thresholds above are then unreachable by construction — at maxJobsPerCompany: 3, high needs 5 postings in 7 days, so every company would come back low and the label would be measuring your cap, not the company. In that case the Actor returns hiringMomentum: "unknown (capped)" with momentumMeasurable: false and keeps the raw counts as lower bounds (openJobCountIsCapped: true). One exception: if the truncated sample already clears the high thresholds, high is still exact — more roles can only push it higher — so high is reported with momentumMeasurable: true. Raise the cap for a measurable label.

Do I need a LinkedIn login or cookies? No. Everything is read from public pages — cookieless.

How do I specify companies? As full LinkedIn company URLs (https://www.linkedin.com/company/stripe/) or bare slugs (stripe). Duplicates and country subdomains are normalised automatically.

A common name resolved to the wrong company — why? A bare slug follows LinkedIn's /company/{slug} page, which for common names may point at a namesake. Check the returned companyDomain to verify the match, and for well-known names pass the exact company URL.

How many jobs are counted per company? Up to maxJobsPerCompany (1–1000). LinkedIn itself exposes roughly the first 1,000 public roles per company.

What does openJobCountIsCapped mean? It's present and true when openJobCount reached your cap. openJobCount and postedLast7d/30d/90d are then lower bounds, oldestJobDate / topDepartments / topLocations describe the sampled roles only, and hiringMomentum is reported as unknown (capped) unless it already clears the high thresholds. Raise maxJobsPerCompany for exact figures.

Are topDepartments official LinkedIn categories? No — they're inferred from job-title keywords. Treat them as a reliable approximation, not LinkedIn's own taxonomy.

What export formats are available? JSON, CSV, Excel, or via the Apify API / integrations.

Is this affiliated with LinkedIn? No. It is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation, and reads only public data.

Troubleshooting

• hiringSignalsCollected: false / no hiring fields at all → the company id couldn't be resolved, so the job feed was never queried (the record carries a note and firmographics only). That is not a "not hiring" verdict. Pass the exact LinkedIn company URL and check companyId is populated.
• hiringMomentum: "unknown (capped)" on every company → your maxJobsPerCompany is too low for the momentum thresholds (see the FAQ). Raise it to 200+ and momentum becomes measurable again.
• A record with error: "company page unreachable" → the public page didn't load (rate-limit or a transient block). Use the default Residential proxy and re-run; smaller batches help.
• Wrong company matched → slug ambiguity. Use the exact /company/{slug} URL and confirm via companyDomain.
• Fewer roles than expected / openJobCountIsCapped: true → raise maxJobsPerCompany (up to 1000) for a complete count on large employers.

Notes, limits & legal (honest)

• Public data only, cookieless. Signals are computed from public LinkedIn postings; a company's public page must be resolvable. If the company id can't be resolved, the record returns firmographics only, hiringSignalsCollected: false and a note — the hiring fields are omitted rather than zeroed, because nothing was measured.
• Slug ambiguity. A bare slug follows /company/{slug}, which for common names may resolve to a different company than intended. The returned companyDomain lets you verify — pass the exact company URL for certainty.
• Capped counts. openJobCount is capped at maxJobsPerCompany; when the cap is hit, openJobCountIsCapped: true is set and every count (including 7/30/90-day velocity) is a lower bound. Because the momentum thresholds then can't be evaluated honestly, hiringMomentum returns unknown (capped) with momentumMeasurable: false — unless the truncated sample already clears the high thresholds, which stays exact.
• Inferred departments. topDepartments is inferred from job titles — a strong approximation, not LinkedIn's internal taxonomy.
• Not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. This Actor reads public data. You are responsible for using the output lawfully — respect LinkedIn's Terms of Service and applicable data-protection law (e.g. GDPR) for your use case and jurisdiction. LinkedIn has no official public jobs/company API; if you need licensed data, use LinkedIn's own products.

Support

Questions, a field you'd like added, or a custom build? Open the Issues tab on this Actor, or email info@foxlabs.com.tr. We reply fast.

If this Actor saves you time, a ⭐ review really helps.

Changelog

0.4.1 — 2026-07-28

• Fixed: every company handle containing a hyphen silently returned nothing. Apify proxy session IDs accept only [\w.~], so a handle like red-bull or palo-alto-networks made the proxy call throw — and the throw was swallowed by the retry loop, so the company came back empty instead of erroring. Handles are now sanitised before the proxy session is created. Most multi-word brands were affected.

0.4 — 2026-07-28

• Momentum no longer measures your cap. When the job sample is truncated at maxJobsPerCompany, the posting-count thresholds behind hiringMomentum become unreachable by construction (at a cap of 3, high needs 5 postings in 7 days — so every company came back low). Truncated samples now return hiringMomentum: "unknown (capped)" with momentumMeasurable: false, and the raw counts are kept as explicit lower bounds via openJobCountIsCapped. A truncated sample that already clears the high thresholds still reports high — more roles can only push it higher. Uncapped runs are unchanged.
• Unresolvable company id no longer reads as "not hiring". When the LinkedIn company id can't be resolved the job feed is never queried, so the record now carries hiringSignalsCollected: false and omits the hiring fields instead of publishing openJobCount: 0 / isHiring: false / hiringMomentum: "not-hiring" — which contradicted the note on the same record.
• founded now actually fills. LinkedIn's public company ld+json carries no foundingDate, so the field was null on every record; the founding year is now read from the visible About block (verified against live company HTML), with a year-sanity guard so a full date can't leak through as 20100101.
• Documented the ownership (KYB) field in the output table.

0.3 — 2026-07-16

• Ultimate beneficial owner (KYB). Each company is now resolved to its ultimate beneficial owner — a real person or a cross-border parent — by walking UK holding-company ownership chains (Companies House PSC + GLEIF). See who really controls the companies you're tracking (e.g. one owned via layers by a US or private-equity parent). Confidence-gated: the owner is attached only when resolved with high confidence, never guessed. New includeOwnership option (on by default). No other hiring-signals scraper links companies to real ownership.

0.2 — 2026-07-05

• Reworked docs: API quick-start, full field table, sample output, input recipes, JS/Python/Make/MCP integrations, FAQ & troubleshooting, and honest legal notes.

0.1

• Initial release. Company-centric hiring signals (open roles, 7/30/90-day velocity, momentum, department & location breakdown, full job list) + inline firmographic enrichment + provenance. Cookieless.

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Part of the foXLabs data platform — public-data company, jobs, procurement, contact & AI-search intelligence scrapers. Browse the full suite at data.foxlabs.com.tr.