LinkedIn Change Monitor

Track hiring changes at specific LinkedIn companies across scheduled Apify runs.

Add public LinkedIn company URLs, choose a watchlist name, and receive structured signals when jobs are added, removed, or changed. The Actor monitors public job information only and does not require LinkedIn cookies, login credentials, candidate profiles, or automated outreach.

Quick start

Paste this into the Apify input editor:

{
  "companyUrls": [
    "https://www.linkedin.com/company/openai/",
    "https://www.linkedin.com/company/anthropic/"
  ],
  "watchlistName": "ai-companies",
  "includeInitialSnapshot": false,
  "maxSignalsPerCompany": 100,
  "filters": {
    "departments": ["Engineering", "Research"],
    "locations": ["San Francisco", "Remote"],
    "seniority": ["Senior", "Staff", "Director"],
    "keywords": ["machine learning", "safety"]
  }
}

The same example is available in examples/input.example.json.

How monitoring works

On the first run, the Actor saves the current jobs as a baseline. On later runs, it compares the latest results with that baseline and returns only changes. After a successful check, it replaces the baseline with the latest data. If a company check fails, its previous baseline is preserved.

Your monitoring history is isolated by your Apify account, watchlist name, company URLs, and filters. Reuse the same values on scheduled runs to continue the same watchlist. Use a different watchlistName for a separate watchlist.

The Actor derives each company ID from the LinkedIn URL, so you do not need to provide internal IDs.

Input fields

Field	Required	Purpose
companyUrls	Yes	One or more exact public LinkedIn company URLs. Example: https://www.linkedin.com/company/openai/.
watchlistName	Yes	A simple name for the saved monitoring history. Example: ai-companies.
includeInitialSnapshot	No	Set true to output all jobs found on the first run. Default: false.
maxSignalsPerCompany	No	Maximum signals emitted for each company in one run. Default: 100.
filters.departments	No	Keep only matching departments. Example: Engineering, Research.
filters.locations	No	Keep jobs whose location contains a listed value. Example: San Francisco, Remote.
filters.seniority	No	Keep matching seniority levels. Example: Senior, Staff, Director.
filters.keywords	No	Keep jobs whose title or department contains a listed term. Example: machine learning, safety.

Omit filters to consider every job returned by the provider.

Output signals

Each dataset item is one signal:

• new_job: a role appeared
• removed_job: a role disappeared after a confirmed provider response
• changed_job: a tracked role changed
• initial_snapshot: a job returned on the first run when enabled
• no_change: the company was checked successfully and nothing changed
• provider_error: the company could not be checked

The Actor also writes a RUN_SUMMARY key-value record with operational counts. Commercial provider costs are not included in that record; Apify calculates billing separately.

HTTP API and live view

The Actor also supports Apify Standby mode. Use the generated Endpoints tab in Apify Console, or call the Standby URL with an Apify API token.

Health check

curl https://YOUR-STANDBY-URL.apify.actor/health \\
  -H "Authorization: Bearer YOUR_APIFY_TOKEN"

Run a monitor check

curl -X POST https://YOUR-STANDBY-URL.apify.actor/monitor \\
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \\
  -H "Content-Type: application/json" \\
  --data @examples/input.example.json

The response contains signals and summary. The request uses the same watchlistName, company URLs, filters, baseline storage, and pricing behavior as a normal Actor run.

Pricing

Charge	Price	When it applies
Actor start	$0.0005 per event	When a run starts; Apify can charge multiple events based on Actor memory
Result	$0.0075	For each signal written to the default dataset
Company record	$0.02	For each company that returns a successful provider response
Provider cost unit	$0.001	Internal usage-based provider charge calculated by the Actor

For example, with the current 2 GB run setting, a run that starts and writes 25 signals has these configured event charges:

(2 × $0.0005) + (25 × $0.0075) + (1 × $0.02) = $0.209

The provider-cost event is charged in $0.001 units after a successful provider run, based on the provider run's measured usage plus the configured margin. It is not included in RUN_SUMMARY; Apify shows the final billing total separately.

Scheduling

Schedule the Actor to run repeatedly with the same watchlistName, company URLs, and filters. This turns the watchlist into a continuing hiring-change feed.

Schedule it in Apify Console

1. Run the Actor once from the Input tab and confirm that the input is correct.
2. Open Schedules in Apify Console and click Create new.
3. Give the schedule a name, such as AI company hiring monitor.
4. Set the frequency and timezone. Daily or weekly runs are usually suitable for hiring monitoring.
5. Click Add, choose this Actor, and paste the same JSON input used for the first run.
6. Check that the same watchlistName, companyUrls, and filters are included.
7. Save and enable the schedule.

Keep the same watchlist name and company URLs for every scheduled run. Changing them starts an isolated monitoring history. Results appear in the run's dataset, and the RUN_SUMMARY record is available in the run's key-value store.

Apify requires the Actor to have been run at least once before it can be scheduled. Schedules can be enabled or disabled from their detail page. See the Apify scheduling guide for API and advanced scheduling options.

Use cases

Recruiting and talent intelligence

Track when target companies open new roles, remove roles, or change job details. Recruiting teams can schedule daily checks and send the dataset signals into their CRM or talent workflow.

Sales and account prioritization

Use hiring activity as an indication that a company may be growing, entering a new market, or investing in a department. Filter by department, location, seniority, or keywords to focus on relevant accounts.

Competitive intelligence

Monitor competitors such as OpenAI, Anthropic, or other public LinkedIn company pages to identify changes in engineering, research, sales, or leadership hiring.

Market research

Build a recurring dataset of hiring signals across a group of companies, locations, or industries and analyze the changes over time.

Integrations and workflow use cases

The main paid value is:

A new job appeared at one of your target accounts, and your workflow was notified automatically.

The sections below describe the recommended path from recurring LinkedIn hiring monitoring to a connected recruiting, sales, or competitive-intelligence workflow.

Scheduled LinkedIn hiring monitoring

Schedules are the foundation of the workflow and are available directly in Apify. Run the Actor daily, weekly, or on a custom interval using the same company URLs, filters, and watchlistName. Each run compares the latest public LinkedIn jobs with the saved baseline and produces hiring-change signals.

This is useful for competitor hiring monitoring, recruiting research, and recurring account intelligence without manually starting every run.

Webhook notifications for hiring changes

A webhook integration would send a notification when the Actor detects a new, removed, or changed job. A receiving endpoint could route the signal to an internal application, automation platform, or alerting service.

The most useful event is a new job at a target account, because it can trigger an immediate follow-up while the hiring activity is current. The Actor currently exposes structured dataset results; webhook delivery is the next integration to add.

n8n LinkedIn hiring-monitor workflow

An n8n example would show how to run the Actor on a schedule, read the dataset output, filter for meaningful hiring signals, and route them to other business systems. For example, an n8n workflow could monitor OpenAI and Anthropic, keep only engineering roles, and send a notification when a new role appears.

This gives users a practical no-code or low-code template for building automated LinkedIn hiring alerts.

Slack hiring alerts

A Slack integration would turn a hiring signal into a message in a chosen channel. A recruiting team could receive new engineering roles in #talent-intelligence, while a sales team could receive hiring activity for target accounts in #sales-signals.

Each alert could include the company, signal type, job title, location, and LinkedIn job URL so users can act without opening the full dataset.

Google Sheets export for hiring research

A Google Sheets integration would append or update hiring signals in a shared spreadsheet. This is useful for teams that want a lightweight hiring tracker, account-research table, or historical view of job growth across companies.

The sheet could contain one row per signal with the company, job title, location, detected date, change type, and job URL.

MCP listing and examples

An MCP integration would make the Actor’s hiring signals available to compatible AI assistants and agent workflows. Users could ask questions such as “Which target accounts added engineering jobs this week?” and use the structured results as context for research or prioritization.

MCP examples should show how to discover the Actor, provide company URLs and filters, and interpret new, removed, changed, and no-change signals.

CRM enrichment from hiring signals

A CRM integration would use hiring activity to enrich company or account records. A new job could update an account’s hiring-signal field, create a research task, or help a sales team prioritize an account that is expanding.

The safest workflow is to send only meaningful changes to the CRM and include the source LinkedIn URL, detected date, job title, and location for review.