LinkedIn Recruiter Scraper

Find recruiters, hiring managers, technical recruiters, and talent acquisition specialists from public LinkedIn profiles — by industry, technology, and location. No cookies, no LinkedIn login, no Sales Navigator seat required.

Paste the recruiter titles you want, optionally narrow by tech stack or industry, and walk away with a clean spreadsheet of names, current titles, companies, locations, and a detected hiring niche for every profile — ready for your CRM, outreach sequencer, or your "who's hiring React engineers right now" list.

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What it does

This actor turns LinkedIn into a recruiter-discovery engine. Specify the titles you care about (Recruiter, Technical Recruiter, Talent Acquisition Manager, Hiring Manager, …), optionally add tech keywords (React, Python, AI) or industries (Healthcare, Fintech, SaaS), and it returns a structured list of matching profiles plus an inferred hiring specialization for each.

Marketed as: "Find Recruiters & Hiring Managers from LinkedIn — by industry, tech stack, and location."

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Who uses this

Job seekers — Find every recruiter hiring for React, Python, or AI roles in your city. Skip the cold-outreach guesswork — message the people whose actual job is to fill the role.

Staffing agencies — Map the in-house recruiting teams at target accounts. Spot understaffed TA teams that are good acquisition targets.

Outreach agencies & sales reps — Build clean, segmented lists of HR / TA leaders to pitch HRTech products to.

HR SaaS founders — Generate ICP lists by niche (AI recruiters, healthcare recruiters, fintech recruiters) for product-led growth and webinar invites.

Recruiting tool comparison & competitive intel — See which recruiters at competing firms specialize in your domain.

Investors — Track hiring intent at portfolio companies by industry vertical.

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What you get back

Each row in the dataset is one recruiter:

{
    "fullName": "Sarah Johnson",
    "firstName": "Sarah",
    "lastName": "Johnson",
    "currentTitle": "Technical Recruiter",
    "company": "TechHire Solutions",
    "hiringNiche": "Frontend Developers",
    "linkedinUrl": "https://www.linkedin.com/in/sarahjohnson",
    "profileImage": null,
    "snippet": "Technical recruiter focused on hiring React developers and AI engineers...",
    "matchedKeyword": "React",
    "recruiterTitleMatched": "Technical Recruiter",
    "locationKeyword": "Germany",
    "timestamp": "2026-05-22T12:00:00Z"
}

The full field list:

Field	What it is
fullName	The recruiter's full name
firstName / lastName	Best-effort split for mail-merge
currentTitle	Detected current title (Recruiter, Technical Recruiter, TA Manager, …)
company	Current company
hiringNiche	Inferred specialization (e.g. "Frontend Developers", "AI / ML Talent")
linkedinUrl	Canonical https://www.linkedin.com/in/<slug> URL
snippet	Raw profile snippet text
matchedKeyword	The industry / tech keyword that surfaced the profile
recruiterTitleMatched	Which recruiter-title bucket the profile matched
locationKeyword	The location filter used (if any)
timestamp	ISO 8601 scrape time

Export to CSV, JSON, Excel, or pipe directly into Zapier, Make, Clay, Apollo, or any HTTP webhook.

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Inputs

Field	Type	What it does
Recruiter Titles	array<string>	Titles to hunt for. Defaults to Recruiter, Technical Recruiter, Talent Acquisition. Add Hiring Manager, Staffing Specialist, Recruiting Lead, Head of Recruitment, Recruitment Consultant, etc.
Industry / Tech Keywords	array<string>	Niche filters (React, Python, AI, SaaS, Healthcare, Fintech, Cybersecurity). Optional.
Locations	array<string>	Country / region / city filters (United States, Berlin, Dubai). Optional.
Maximum Profiles	integer	Stop after collecting N unique profiles. Default 100.
Search Language	string	Language hint. Default en.
Deduplicate Results	boolean	Drop duplicate LinkedIn URLs across the run. Default true.

The actor expands every (keyword × location) combination using the supplied title bucket. So 3 tech keywords × 2 locations = 6 internal searches, all deduplicated into one clean dataset.

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Hiring-niche detection

Each profile gets a hiringNiche label inferred from the keyword you searched, the LinkedIn headline, and the snippet text. A non-exhaustive list of the buckets:

Trigger words	Detected niche
React, Vue, Angular, Svelte, frontend	Frontend Developers
Python, Django, FastAPI	Python Engineers
Java, Spring Boot, Kotlin	Java / JVM Engineers
Golang, Rust, C++	Systems Engineers
Node.js, TypeScript, full-stack	Full-Stack Engineers
iOS, Swift, Android, Flutter, React Native	Mobile Engineers
DevOps, SRE, Kubernetes, Terraform, AWS, GCP, Azure	DevOps / Cloud Engineers
Data engineer, Data scientist, Snowflake, Databricks	Data Engineers
AI, ML, LLM, Generative AI, NLP, Computer Vision	AI / ML Talent
Cybersecurity, InfoSec, Pentest, SOC Analyst	Security Professionals
UX, UI, Product Design, Figma	Design Talent
Healthcare, Medical, Pharma, Biotech	Medical / Healthcare Hiring
Fintech, Banking, Asset Management, Trading	Finance Industry
SaaS, B2B SaaS	SaaS Hiring
Shopify, E-commerce, Retail, Marketplace	E-commerce Hiring
Marketing, Growth, SEO, SEM	Marketing Talent
Sales, Account Executive, BDR, SDR	Sales Talent
Blockchain, Crypto, Web3, Solidity	Web3 / Crypto Talent

If you typed a keyword that's not in the rule list, the actor still surfaces it as <Keyword> Hiring so the field is never empty when you asked for something specific.

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Example use cases

Job seekers — recruiterTitles: ["Technical Recruiter", "Talent Acquisition"], keywords: ["React"], locations: ["Berlin", "London"]. Result: every TA pro hiring React talent in Berlin and London, with a hiringNiche: "Frontend Developers" tag.

Staffing agency prospecting — recruiterTitles: ["Head of Recruitment", "Talent Acquisition Manager"], keywords: ["SaaS"], locations: ["United States"]. Pull every in-house TA leader at US SaaS companies and pitch your staffing services.

HRTech sales — recruiterTitles: ["Recruiter"], keywords: ["Healthcare", "Pharma", "Biotech"], locations: ["United States", "Germany"]. Build an ICP list for a recruiting product targeting healthcare HR.

Outbound to in-house recruiters — recruiterTitles: ["Technical Recruiter"], keywords: ["AI", "ML"]. Find everyone hiring AI/ML engineers, then enrich emails via Clay or Apollo.

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Tips that save you time and money

• Preview with a low cap. Set Maximum Profiles to 25 for your first run to verify the queries return the kind of recruiters you expect.
• Niche detection works best when you supply a keyword. Searching recruiter alone returns a generic bucket. Adding React or Healthcare lights up the hiringNiche field for everyone the keyword matched.
• Stack multiple title buckets in one run. ["Technical Recruiter", "Talent Acquisition", "Hiring Manager"] runs faster than three separate runs because the start fee is charged once.
• Locations are loose by design — they're matched as phrases that appear anywhere on the profile, so you'll get some noise. Run per-country if you need clean segmentation.
• Deduplication is on by default. Leave it on unless you specifically want to see overlap across keywords.

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Pricing — pay only for what you extract

Apify Plan	Start Fee	Per Profile
Free / Bronze	$0.0025	$0.0025
Silver	$0.0025	~$0.0022
Gold	$0.0025	~$0.0020
Platinum	$0.0025	~$0.0018
Diamond	$0.0025	~$0.0015

Affordable lead generation at scale without expensive LinkedIn tools 🚀

• 25 recruiters for a test run: about $0.06
• 250 React recruiters across the US & Canada: $0.63
• 2,000 recruiters across 10 niches: about $5.00

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Frequently asked

Do I need a LinkedIn account or Sales Navigator? No. The actor never logs into LinkedIn, never asks for cookies, and never touches your account. It returns publicly visible profile data only.

Will this get me LinkedIn-banned? No. There's no account activity for LinkedIn to flag.

How accurate is the hiringNiche field? The detector matches against ~20 niche buckets covering frontend, backend, DevOps, data, AI/ML, security, design, healthcare, fintech, marketing, sales, Web3, and more. If you searched with an explicit keyword (React, Pharma, …), expect 80%+ accuracy. If you searched with no keyword, the field will often be null because there's no signal to classify on.

Will I get duplicates if I run it twice? Within a single run, no — dedup is on by default. Across runs, yes — track previously seen linkedinUrl values in your own pipeline.

Can I schedule this? Yes. Apify Schedules let you run it weekly to catch new recruiters entering an industry or location.

The actor returned 0 results — what's wrong? (1) Try removing the Locations filter to see if the issue is over-narrowing. (2) Broaden your title list — add Recruiter alongside Talent Acquisition. (3) Use a more common keyword (SaaS instead of Vertical SaaS).

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A note on responsible use

This actor extracts data that is already publicly visible on LinkedIn. It does not bypass authentication, log into accounts, scrape private profiles, or pull anything beyond what any anonymous visitor could see. The legal foundation for scraping public profile data was reinforced in hiQ Labs v. LinkedIn (9th Circuit, 2022).

That said: scraped data is still personal data. Don't use it for spam, harassment, or anything that violates GDPR, CCPA, or similar laws in your jurisdiction. Treat the recruiters in your dataset like the experienced humans they are — they'll notice cold-blast outreach immediately.

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Drop it into your workflow

• Apify Schedules → run weekly to surface new recruiters joining your niche
• Zapier / Make → push new rows into HubSpot, Salesforce, Notion, or Google Sheets
• Apify Webhook → POST results to your server for custom enrichment
• Pair with a LinkedIn Profile Scraper → use linkedinUrl to enrich each recruiter with full work history
• Pair with an email-finder → push firstName + lastName + company into Clay, Apollo, or Hunter to resolve emails

Run it once with a small maxResults to see what you get. The first 25 profiles are usually cheap enough to taste-test before committing.