AI/ML Hiring Signal Monitor — Track Companies Scaling AI Teams

AI/ML Hiring Signal Monitor — Track Companies Scaling AI Teams

Track which companies are actively hiring for AI/ML roles on LinkedIn right now — machine learning engineer, AI researcher, LLM engineer, applied scientist — without logging in or managing cookies. Enter one or more job titles (keywords) and locations — the actor returns a fresh feed of matching job postings with company name, job URL, and posting date.

Why this matters for VCs and AI recruiters

AI Investment & VC Intel: a company posting 5+ "Machine Learning Engineer" or "LLM Engineer" roles this week is heavily scaling its AI initiatives. This is a leading indicator of AI investment, product direction, and growth — catch it before public announcements.

AI Recruiting: track when a competitor or target company starts posting AI roles in a new city or function. Source candidates at expanding AI teams or pitch your AI-talent recruiting agency services when they have maximum urgency.

Market research: monitor hiring velocity in the AI/ML category (e.g. "applied scientist" in San Francisco) to track demand trends over time.

Output — two record types

The run emits job records (one per posting) and, on top of them, company hiring-signal records — the differentiator: an aggregated, firmographics-enriched B2B prospect list of the companies that are actively scaling. Filter by record_type.

Job record fields

Field	Description
title	Job title exactly as listed
company / company_url	Company name + LinkedIn company page URL
location	Location string from the listing
posted_date	ISO date the job was posted
job_url / listing_id	Direct link + LinkedIn's internal job ID
seniority	Inferred level: Senior, Junior, Manager, Director, etc.
description_text	Full job description (detail enrichment)
employment_type	Full-time / Contract / Part-time (detail)
job_function	LinkedIn job function (detail)
industries	Company industry for the role (detail)
applicants	Applicant count — hiring-urgency signal (detail)
salary	Salary when LinkedIn surfaces it (detail)
apply_url	Offsite application URL when present (detail)
skills_mentioned	Skills from the posting text (e.g. python, pytorch, llms)
search_keyword / search_location	Which query produced this result
parse_confidence	Data completeness score 0–1

Company hiring-signal record (record_type: "company_hiring_signal")

Emitted for every company with at least minOpenRoles (default 2) open roles in the run.

Field	Description
company / company_url	Company + LinkedIn page
open_roles_in_run	How many open roles matched — the scaling signal
hiring_velocity	emerging / active / high / aggressive (by role count)
roles	List of the open job titles
hiring_locations	Distinct locations they're hiring in
seniority_breakdown	Count of roles by inferred seniority
functions_hiring	Distinct job functions being hired
industries	Industries inferred from the roles
total_applicants / avg_applicants_per_role	Aggregate demand across the roles
earliest_post / latest_post	Posting-date range (recency of the hiring push)
company_size / company_size_tier	Employee band + tier (Startup→Enterprise)
company_followers	LinkedIn follower count
company_industry / company_hq / company_website	Firmographics from the company page

Sample output

[
  {
    "title": "Machine Learning Engineer",
    "company": "Anthropic",
    "company_url": "https://www.linkedin.com/company/anthropicresearch",
    "location": "San Francisco, CA",
    "posted_date": "2026-06-08",
    "job_url": "https://www.linkedin.com/jobs/view/3987654321",
    "listing_id": "3987654321",
    "seniority": null,
    "job_function": "Engineering and Information Technology",
    "industries": "Software Development",
    "applicants": 200,
    "skills_mentioned": ["python", "pytorch", "transformers", "llms", "cuda"],
    "parse_confidence": 1.0
  },
  {
    "record_type": "company_hiring_signal",
    "company": "Anthropic",
    "company_url": "https://www.linkedin.com/company/anthropicresearch",
    "open_roles_in_run": 3,
    "hiring_velocity": "active",
    "roles": ["Machine Learning Engineer", "Senior AI Researcher", "LLM Engineer"],
    "hiring_locations": ["San Francisco, CA", "Seattle, WA"],
    "seniority_breakdown": {"Senior": 1, "Mid-Senior level": 2},
    "functions_hiring": ["Engineering and Information Technology", "Research"],
    "total_applicants": 600,
    "avg_applicants_per_role": 200.0,
    "company_size": "501-1,000 employees",
    "company_size_tier": "Mid-market",
    "company_followers": 1041922,
    "company_hq": "San Francisco, California, US"
  }
]

Input

Field	Required	Default	Notes
keywords	Yes	—	List of job title keywords. Each runs as a separate search.
locations	No	Global	List of location strings (city, country, region).
postedWithin	No	week	24h, week, or month.
maxItems	No	100	Total results cap (max 1000).
proxyConfiguration	No	RESIDENTIAL	Apify RESIDENTIAL proxy recommended.

Technical notes

• Uses LinkedIn's public guest jobs API (no login, no cookies required).
• Apify RESIDENTIAL proxy for reliability.
• Deduplicates results across keyword/location combinations.
• Results capped at 1000 per run; use multiple runs for larger volumes.

💰 Pricing & how we compare

Pay-per-result (PPE): from $3.00 / 1K results. You are billed per job-result actually returned — plus the tiny apify-actor-start fee Apify waives for short runs. No subscription, no API key, no proxy fee on top.

Our edge: Company hiring-velocity signal (who is ramping) · per-job detail enrichment · no cookies, no login.

Pricing examples (pay only for what you get, minus Apify's 20%):

Volume	Cost
100 results	$0.30
1,000 results	$3.00
10,000 results	$30.00

How rivals price the same job (live Apify Store, checked 2026-06-09)

Actor	Their price	What they lack vs us
agentx/linkedin-jobs-scraper	$2.80/1K	5★ full taxonomy, but no company-velocity hiring signal
scrapemint/linkedin-jobs-scraper-pro	FREE	56% success rate — unreliable at scale

Prices above are competitors' live Store prices at the time of writing; ours is set to sit just below the strongest comparable while returning richer, quality-scored data.

🤖 Use with AI agents (MCP)

This actor is agent-ready (category MCP_SERVERS). Point any MCP client (Claude Desktop, Cursor, n8n AI, LangGraph) at it:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?actors=bovi/ai-ml-hiring-signal-monitor",
      "headers": { "Authorization": "Bearer <YOUR_APIFY_TOKEN>" }
    }
  }
}

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Linkedin Profile Scraper
• PagesJaunes Directory Scraper
• Skip Trace People Finder
• Trustpilot Reviews Scraper
• Upwork Talent Scraper
• Website Contact Extractor

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.