Public Candidate Finder — LinkedIn Recruiter Alternative

Find public candidate leads using role, location, employer, and professional-search filters. Export LinkedIn profile URLs, professional context, source evidence, and timestamps for recruiter review. For recruiters, each dataset row represents one public candidate lead and the professional search context used to find it.

Workflow: put the results to work

Translate the hiring brief into a role query and the relevant location or employer filters. Inspect each public profile lead and the applied search context, then move approved records into your recruiting process. Every result needs human review before a candidate decision.

A recruiter workflow

An agency recruiter starts with machine learning engineer, adds London and several senior-title alternatives, and caps the run at 25 candidates. The Actor requests a stable provider session, deduplicates profiles, validates every complete public candidate record, and stores it in the dataset. Next, the recruiter reviews the visible role and location context, confirms relevance manually, and routes selected records into their approved recruiting workflow.

Public candidate data

Field	Meaning
fullName	Public professional name returned for the profile
profileUrl	Public LinkedIn profile URL
currentTitle / currentCompany	Current professional context when returned
location	Public professional location text when returned
matchEvidence	Visible or applied professional search-filter context
source / collectedAt	Provider provenance and collection time
humanReviewRequired	Always true; every record needs human review

The data source is an owner-managed HarvestAPI public lead-search route. The Actor accepts no LinkedIn account, cookie, session, or contact credential from the caller. Public sourcing records are intended for authorised recruitment research and manual review; human-led workflows remain responsible for all employment decisions.

Quick start input

{
  "searchQuery": "machine learning engineer",
  "currentJobTitles": ["Senior Machine Learning Engineer", "Staff Machine Learning Engineer"],
  "locations": ["London"],
  "recentlyChangedJobs": false,
  "maxResults": 25
}

Use currentCompanies or excludeCurrentCompanies only with public LinkedIn company URLs. Add postedOnLinkedin when a recent public posting signal supports the sourcing workflow. The Actor stores applied filters with every record so a recruiter can understand the exact scope that produced the dataset.

Example output

{
  "recordType": "public_recruiting_candidate_lead",
  "candidateId": "public-provider-id",
  "fullName": "Ada Lovelace",
  "profileUrl": "https://www.linkedin.com/in/example-person",
  "currentTitle": "Senior Machine Learning Engineer",
  "currentCompany": "Example Ltd",
  "currentCompanyUrl": "https://www.linkedin.com/company/example",
  "location": "London, England",
  "openProfile": false,
  "premiumProfile": false,
  "matchEvidence": ["search_query_visible", "title_visible", "location_visible"],
  "searchQuery": "machine learning engineer",
  "appliedFilters": {
    "currentJobTitles": ["Senior Machine Learning Engineer"],
    "locations": ["London"],
    "currentCompanies": [],
    "recentlyChangedJobs": false,
    "postedOnLinkedin": false
  },
  "source": "harvest_api_public_linkedin_lead_search",
  "collectedAt": "2026-01-01T00:00:00.000Z",
  "humanReviewRequired": true,
  "useLimit": "Use only as public sourcing research. Do not use this record to make automated employment, eligibility, or suitability decisions."
}

API and dataset readback

Start a bounded run from an authorised server-side client, then read the default dataset and the compact terminal record.

curl -X POST 'https://api.apify.com/v2/acts/khadinakbar~linkedin-recruiter-alternative/runs?token=YOUR_APIFY_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{"searchQuery":"machine learning engineer","locations":["London"],"maxResults":25}'

Each run writes OUTPUT and RUN_SUMMARY alongside the candidate dataset. OUTPUT gives an actionable terminal outcome and charge counts; RUN_SUMMARY adds page, deduplication, provider, cost-cap, and warning context. Use dataset pagination for larger result sets or narrow the professional criteria for a smaller review set.

AI-agent prompt card

Find up to 25 public data-engineer profiles in Berlin for recruiter review. Return the public name, current title, current company, profile URL, location, source, and collection time. Keep every result marked for human review, report the terminal outcome, and read the dataset after the run.

An AI agent can use the declared input and output schema to prepare a bounded request, inspect the terminal outcome, and retrieve the dataset. The agent should preserve the source and collection-time fields, communicate the event cost and platform usage, respect pagination, and hand the record set to a human recruiter for review.

Best results guidance

Start with a specific role and one market, then add title or public-company filters only when the initial dataset needs more focus. Keep result caps aligned with the number of records a recruiter can review, confirm the visible public context, and retain the source and collection time alongside downstream records. A fresh scheduled run can support an authorised recurring sourcing workflow once its output is reviewed and maintained by the operator.

Builder's note

I designed this Actor around the narrow public sourcing step because that is where a structured, source-linked dataset provides value without copying a recruiting suite. The output intentionally keeps public professional context, collection provenance, and a human-review boundary together so downstream systems have less ambiguous data to work with.

Responsible use

Use this workflow only for authorised, lawful professional sourcing and in line with applicable laws, privacy obligations, and site terms. Treat public profile data carefully, retain it only as long as needed for the defined recruiting purpose, and keep a qualified human responsible for every employment-related decision.

How this workflow compares with LinkedIn Recruiter

Decision area	This Actor	LinkedIn Recruiter	Practical routing
Scope and workflow	Bounded public candidate sourcing and structured export	Licensed recruiting suite with broader workflow features	Use this Actor for the initial public sourcing-export step; use Recruiter for the wider suite workflow
Billing and cost	Pay per event plus platform usage; see the Pricing tab for current rates.	Subscription and contract pricing vary by offering, region, and terms	Use the Actor price for a bounded run; evaluate Recruiter plans for ongoing suite access
Output usability and efficiency	A dataset row keeps profile URL, professional context, source, collection time, and review boundary together	Recruiter provides its own product workflows and exports	Choose the record shape that keeps the recruiter review process clear and auditable
Input access	Professional filters with no caller LinkedIn account credential	Licensed account-based recruiting tools	Select the access model that matches the approved recruiting workflow
Integration and automation	Declared Apify API and dataset output for programmatic readback	Recruiter supports selected ATS integrations and team features	Pair this Actor with an approved downstream system; use Recruiter where its ATS and team integration is central

LinkedIn Recruiter is a trademark of its owner. This independent Actor is not affiliated with, associated with, or endorsed by LinkedIn. It offers a focused public sourcing workflow; LinkedIn Recruiter remains the appropriate product for teams whose workflow centres on its licensed recruiting suite.

Pricing and run costs

This Actor uses Pay per event plus Apify platform usage. The Pricing tab lists the current event rates and billing terms.

Event	Billing unit	When it applies
apify-actor-start	Actor Start	Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).
candidate-lead-found	Public candidate lead found	One complete public professional candidate lead persisted with provenance and a human-review boundary.

Run cost combines the charged events and Apify platform usage. Review the run charge limit and requested result count before starting.

Connect an AI agent

Use the Apify MCP configurator to choose an available client connection. Inspect this Actor’s current input schema and required credentials before running it.