# LinkedIn Recruiter / Job Poster Finder (`coregent/linkedin-recruiter-job-poster-finder`) Actor Find recruiters, hiring managers, and job posters from public LinkedIn jobs - one flat lead row per person, with job context (matched job, company, role keyword, location), relevance score, and reason tags. No LinkedIn login or cookies required. - **URL**: https://apify.com/coregent/linkedin-recruiter-job-poster-finder.md - **Developed by:** [Delowar Munna](https://apify.com/coregent) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.40 / 1,000 lead-results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## LinkedIn Recruiter / Job Poster Finder ![LinkedIn Recruiter / Job Poster Finder](https://raw.githubusercontent.com/coregentdevspace/linkedin-recruiter-job-poster-finder-assets/main/thumbnail-linkedin-recruiter-job-poster-finder.jpg) Find **recruiters, hiring managers, and job posters** behind public LinkedIn job listings — clean, flat, CSV-ready **people leads** with matched-job context (job title, company, location, posted date), a transparent relevance score, and reason tags. Built for **staffing agencies, recruiting teams, sales / lead-gen, and B2B research**. **No LinkedIn login, no cookies, no session IDs.** The actor uses LinkedIn's public guest jobs surface over HTTP. You pay one flat event per unique lead row pushed to the dataset. ### ✨ Why this scraper - **People-first, not job-first** — every row is a person/lead with hiring context (matched job, company, role keyword, location, source type), not a job listing. - **Three discovery modes** — `jobs_with_posters` (default), `recruiter_search` (recruiter-titled searches harvested for people), `combined` (both, deduped). - **30 flat fields** — person identity, source type, matched-search context, optional job context, relevance score + reason tags. No nested objects; drops straight into Sheets/Excel/CRMs. - **Pay-Per-Event** — one flat `lead-result` event per saved unique lead. Duplicates, person-less jobs, and filtered rows are never charged. - **No login / cookies / sessions** — just keywords (or pasted LinkedIn job URLs). - **Transparent relevance score (PRD §7)** — rule-based, no AI, explained below. --- ### 🚀 Quick start — sample inputs #### Example 1 — jobs with posters (default) ```json { "mode": "jobs_with_posters", "keywords": ["software engineer", "data analyst"], "locations": ["Sydney", "Melbourne"], "datePosted": "past_week", "remoteFilter": "any", "maxResults": 100, "maxJobsToInspect": 250, "includeJobContext": true, "deduplicate": true, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Example 2 — combined (jobs + recruiter searches, deduped) ```json { "mode": "combined", "keywords": ["software engineer"], "locations": ["Sydney"], "titleKeywords": ["recruiter", "talent acquisition", "hiring manager"], "countryCode": "au", "datePosted": "past_month", "remoteFilter": "remote", "companyNames": [], "maxResults": 200, "maxJobsToInspect": 500, "includeJobContext": true, "deduplicate": true, "proxyConfiguration": { "useApifyProxy": true } } ``` > Provide **at least one** of `keywords` (with optional `locations`) or `jobUrls`. The actor blocks Apify Residential proxy; if you need residential routing, supply your own provider via `proxyConfiguration.proxyUrls`. See **🚦 Proxy policy** below. *** ### 📦 Output The dataset has one view: **Recruiter / job-poster leads** — a 30-column flat table. ![Recruiter / job-poster leads — table view](https://raw.githubusercontent.com/coregentdevspace/linkedin-recruiter-job-poster-finder-assets/main/linkedin-recruiter-job-poster-finder-output-recruiter-job-poster-leads-table-view.png) #### Output fields (30) `lead_name`, `lead_first_name`, `lead_last_name`, `lead_title`, `lead_company`, `lead_location`, `linkedin_profile_url`, `linkedin_profile_id`, `source_type`, `matched_keyword`, `matched_location`, `matched_company`, `job_title`, `job_company`, `job_location`, `job_url`, `job_id`, `job_posted_at_text`, `job_posted_days_ago`, `employment_type`, `workplace_type`, `applicant_count_text`, `is_direct_job_poster`, `is_recruiter_like`, `relevance_score`, `relevance_label`, `reason_tags`, `search_url`, `input_url`, `scraped_at`. #### Sample record — Recruiter / job-poster leads ```json { "lead_name": "Matthew Lane", "lead_first_name": "Matthew", "lead_last_name": "Lane", "lead_title": "Direct message the job poster from swipejobs", "lead_company": "swipejobs", "lead_location": "Talent Acquisition Manager", "linkedin_profile_url": "https://www.linkedin.com/in/matthew-lane-a9970b157", "linkedin_profile_id": "matthew-lane-a9970b157", "source_type": "job_poster", "matched_keyword": "software engineer", "matched_location": "Sydney", "matched_company": "swipejobs", "job_title": "Graduate Software Engineer", "job_company": "swipejobs", "job_location": "North Sydney, New South Wales, Australia", "job_url": "https://www.linkedin.com/jobs/view/4413191895", "job_id": "4413191895", "job_posted_at_text": "2 weeks ago", "job_posted_days_ago": 14, "employment_type": "Full-time", "workplace_type": null, "applicant_count_text": "Over 200 applicants", "is_direct_job_poster": true, "is_recruiter_like": false, "relevance_score": 65, "relevance_label": "Medium", "reason_tags": "direct_job_poster|matched_job|profile_url_available|company_available|keyword_match", "search_url": "https://www.linkedin.com/jobs/search/?keywords=software+engineer&location=Sydney&sortBy=R", "input_url": null, "scraped_at": "2026-05-26T04:06:05.664Z" } ``` *** ### 🎯 Relevance score (PRD §7) Transparent rule-based score (0–100) computed from extracted fields — no AI, no external enrichment. | Signal | Points | | ----------------------------------------------------------- | -----: | | Direct job poster **or** hiring-team contact visible | +35 | | Title/headline contains recruiter / hiring / talent keyword | +25 | | Matched to a specific job posting | +15 | | Job posted within the last 7 days (where parsed) | +10 | | LinkedIn profile URL available | +10 | | Company available | +5 | Score is capped at 100. **Labels**: `High` (80–100) · `Medium` (60–79) · `Low` (30–59) · `Weak` (0–29). `reason_tags` is a **pipe-separated** string (CSV-safe). Vocabulary: `direct_job_poster`, `hiring_team_visible`, `recruiter_title`, `talent_acquisition_title`, `matched_job`, `recent_job`, `profile_url_available`, `company_available`, `keyword_match`. *** ### 💰 Pricing **Pay-Per-Event**. One flat event per saved row (final per-event price is configured on the Apify console): | Event | Charged when | | ------------- | ------------------------------------------------------------------------------------------------------- | | `lead-result` | Once per unique recruiter / job-poster lead row that passed all filters and was written to the dataset. | So your bill is simply `results_saved × price_per_event`. The actor honors the user-configured per-run spending cap (Apify `eventChargeLimitReached`): it both caps how many results it collects up-front to what the limit can pay for, and stops cleanly the moment the cap is reached during charging. Not charged: - Duplicates (deduplicated by profile ID/URL and normalized name+company). - Rows filtered out by company / title / date / workplace filters. - Job pages with **no visible person identity** (PRD §6 — leads must include at minimum a profile URL or a name + company/title). - Failed or blocked requests. #### 🚦 Proxy policy Use **Apify Datacenter** proxy or **no proxy** for normal runs — both work for LinkedIn's public guest jobs surface at this actor's conservative concurrency. **Apify Residential proxy is not supported.** The actor will fail at startup if `proxyConfiguration.apifyProxyGroups` includes `RESIDENTIAL`. Reason: in pay-per-event actors, residential bandwidth (~/GB) is billed to the developer, not the run user, so a single bandwidth-heavy run could exceed the per-result event revenue. If you genuinely need residential routing, supply your own residential provider via the proxy editor's **Custom proxy URLs** field — that traffic goes through your provider, not Apify, and is unaffected: ``` http://user:pass@proxy.iproyal.com:12321 http://user:pass@proxy.brightdata.com:22225 http://user:pass@proxy.oxylabs.io:7777 ``` *** ### 📊 Run summary After each run, a `RUN_SUMMARY` entry is written to the key-value store: ```json { "inputs_total": 9, "searches_total": 8, "job_urls_total": 1, "successful_inputs": 9, "failed_inputs": 0, "jobs_found": 412, "jobs_inspected": 247, "raw_leads_found": 138, "results_saved": 92, "duplicates_removed": 31, "filtered_out": 124, "charged_events": 92, "blocked_requests": 4, "retry_count": 11, "invalid_urls": 0, "runtime_seconds": 318, "scraped_at": "2026-05-24T06:00:00.000Z" } ``` `charged_events` equals the number of successfully saved unique lead rows. *** ### ⚙️ Filters | Filter | Stage | Effect | | ------------------ | -------------- | --------------------------------------------------------------------------------------- | | `mode` | Source builder | `jobs_with_posters` / `recruiter_search` / `combined` — decides which searches are run. | | `datePosted` | Source + post | `any` / `past_24h` / `past_week` / `past_month`. Enforced at LinkedIn where stable. | | `remoteFilter` | Source + post | `any` / `remote` / `hybrid` / `onsite`. | | `companyNames` | Pre + post | Case-insensitive substring on `lead_company`, `matched_company`, `job_company`. | | `titleKeywords` | Post | Case-insensitive substring on `lead_title`. Direct posters always pass (PRD §8). | | `maxResults` | Both stages | Caps saved unique lead rows across the whole run. | | `maxJobsToInspect` | Crawl-time | Safety cap on detail pages opened. | | `deduplicate` | Both stages | Profile ID/URL + name+company keys (recommended ON). | Filters are applied **before** any dataset push or event charge. *** ### 🚧 Limitations (V1) - **Public guest data only**: no login, cookies, or member-only content. Many LinkedIn job pages do **not** publicly expose the "Meet the hiring team" / job-poster module to logged-out viewers. When no person is visible, the row is dropped per PRD §6 — you are never charged for person-less jobs. - **`lead_location` is `null`** unless the public detail fragment exposes the person's location (rare). - **No** people-search crawling: `recruiter_search` does not target LinkedIn's auth-walled people search. It expands the recruiter title set into the same guest-jobs surface, then harvests the visible hiring contact behind each posting. - **No** email enrichment, company-website crawling, or AI scoring. - LinkedIn guest pagination tops out around ~1,000 results per search source; narrow searches return more coverage than broad ones. - `maxResults` caps **saved unique** rows across the whole run (not per query); `maxJobsToInspect` is a separate safety cap on detail visits. *** ### ❓ FAQ **Do I need a LinkedIn account or cookies?** No. The actor only uses LinkedIn's public guest jobs endpoints. **Why are some runs lean?** Public LinkedIn often hides the hiring-team module from logged-out viewers. The actor only saves rows where a person identity is visible (PRD §6). Try broader keywords/locations or use `combined` mode to maximize coverage. **How is `is_direct_job_poster` different from `is_recruiter_like`?** `is_direct_job_poster` means LinkedIn visibly attaches the person to the job posting (the "message the job poster" / direct-poster module). `is_recruiter_like` is a title-based heuristic (`recruiter` / `talent acquisition` / `hiring manager`...) and works for both direct posters and visible hiring-team members. **Can I paste a LinkedIn jobs search URL?** Yes — put it in `jobUrls`. Both `/jobs/view/` and `/jobs/search/?keywords=...` URLs are supported. Search URLs preserve their filters and the actor paginates them. **Can I export to CSV?** Yes — every field is flat (no nested objects). Use Apify's CSV / Excel export, or call the dataset API with `format=csv`. `reason_tags` is intentionally pipe-separated to remain a single CSV cell. **Will I get blocked?** The actor uses conservative concurrency (1–5), realistic headers, session rotation, and retry/backoff. Default Apify Proxy is sufficient for typical small/medium runs. *** ### 🛠️ Technical notes - **Stack**: Node.js 22 · Apify SDK 3 · Crawlee `CheerioCrawler` · Cheerio + native fetch. No browser. - **Endpoints**: LinkedIn public guest `seeMoreJobPostings` (search) and `jobPosting` (detail). - **Concurrency**: `min=1`, `max=5` (conservative; tune after real runs). - **Memory**: 1 GB min · 2 GB default · 4 GB max. - **Proxy**: Apify Proxy enabled by default; custom configs accepted; Apify Residential rejected at startup. # Actor input Schema ## `mode` (type: `string`): How to find leads. `jobs_with_posters` searches jobs by your keywords/URLs and extracts visible job posters / hiring-team people. `recruiter_search` searches jobs with recruiter-style title keywords (yours, or a sensible default set) and extracts the people behind those postings. `combined` runs both and deduplicates. ## `keywords` (type: `array`): Job / role keywords, e.g. "software engineer", "data analyst". Each keyword is paired with each location. Required unless `jobUrls` is provided. ## `locations` (type: `array`): Search locations, e.g. "Sydney", "United States", "London". Each keyword is paired with each location. Leave empty to search without a location filter. ## `jobUrls` (type: `array`): Direct public LinkedIn job URLs to inspect for visible posters / hiring-team data. Processed before keyword search. Invalid URLs are skipped and counted in the run summary. ## `maxResults` (type: `integer`): Maximum number of saved unique recruiter / job-poster lead rows across the whole run. Range 1-10000. ## `maxJobsToInspect` (type: `integer`): Safety cap on job detail pages opened to look for poster / hiring-team data. Should normally be greater than or equal to Max saved leads. ## `companyNames` (type: `array`): Keep only leads whose company (lead, matched, or job company) contains one of these terms (case-insensitive). Leave empty to keep all. ## `titleKeywords` (type: `array`): Filter person rows by visible title, e.g. "recruiter", "talent acquisition", "hiring manager". In `recruiter_search` / `combined` modes these also seed the recruiter-style searches; leave empty to use a sensible default recruiter title set. ## `countryCode` (type: `string`): Optional ISO-like country code (`us`, `au`, `gb`, `ca`) used to localize searches where the public LinkedIn URL supports it. Unknown values are ignored. ## `datePosted` (type: `string`): Filter jobs by posting recency. Applied at the LinkedIn source where stable. ## `remoteFilter` (type: `string`): Filter by work arrangement where visible. Applied at the LinkedIn source where stable; otherwise post-filtered. ## `includeJobContext` (type: `boolean`): Include the matched-job fields (job\_title, job\_company, job\_location, job\_url, job\_id, posted date, employment type, workplace type, applicants count) alongside each person row. Turn off for a leaner person-only export. ## `deduplicate` (type: `boolean`): Remove duplicate people across jobs and searches using profile URL / profile ID and normalized name + company. Recommended ON so you are not charged for duplicates. ## `proxyConfiguration` (type: `object`): Apify Proxy configuration. Defaults to Apify Proxy enabled. Apify Residential is NOT supported and will fail the run at startup; if you need residential routing, supply your own provider via Custom proxy URLs (proxyUrls). ## Actor input object example ```json { "mode": "jobs_with_posters", "keywords": [ "software engineer", "data analyst" ], "locations": [ "Sydney" ], "jobUrls": [], "maxResults": 100, "maxJobsToInspect": 250, "companyNames": [], "titleKeywords": [ "recruiter", "talent acquisition", "hiring manager" ], "countryCode": "", "datePosted": "any", "remoteFilter": "any", "includeJobContext": true, "deduplicate": true, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `overview` (type: `string`): Flat 30-field table of every recruiter / hiring-manager / job-poster lead pushed to the dataset, including person identity, source type, matched search context, optional job context, transparent relevance score and reason tags. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "software engineer", "data analyst" ], "locations": [ "Sydney" ], "jobUrls": [], "titleKeywords": [ "recruiter", "talent acquisition", "hiring manager" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("coregent/linkedin-recruiter-job-poster-finder").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "keywords": [ "software engineer", "data analyst", ], "locations": ["Sydney"], "jobUrls": [], "titleKeywords": [ "recruiter", "talent acquisition", "hiring manager", ], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("coregent/linkedin-recruiter-job-poster-finder").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "software engineer", "data analyst" ], "locations": [ "Sydney" ], "jobUrls": [], "titleKeywords": [ "recruiter", "talent acquisition", "hiring manager" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call coregent/linkedin-recruiter-job-poster-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=coregent/linkedin-recruiter-job-poster-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Recruiter / Job Poster Finder", "description": "Find recruiters, hiring managers, and job posters from public LinkedIn jobs - one flat lead row per person, with job context (matched job, company, role keyword, location), relevance score, and reason tags. No LinkedIn login or cookies required.", "version": "1.0", "x-build-id": "lU7ofjxhzeTP9O22h" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/coregent~linkedin-recruiter-job-poster-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-coregent-linkedin-recruiter-job-poster-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/coregent~linkedin-recruiter-job-poster-finder/runs": { "post": { "operationId": "runs-sync-coregent-linkedin-recruiter-job-poster-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/coregent~linkedin-recruiter-job-poster-finder/run-sync": { "post": { "operationId": "run-sync-coregent-linkedin-recruiter-job-poster-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Discovery mode", "enum": [ "jobs_with_posters", "recruiter_search", "combined" ], "type": "string", "description": "How to find leads. `jobs_with_posters` searches jobs by your keywords/URLs and extracts visible job posters / hiring-team people. `recruiter_search` searches jobs with recruiter-style title keywords (yours, or a sensible default set) and extracts the people behind those postings. `combined` runs both and deduplicates.", "default": "jobs_with_posters" }, "keywords": { "title": "Search keywords", "type": "array", "description": "Job / role keywords, e.g. \"software engineer\", \"data analyst\". Each keyword is paired with each location. Required unless `jobUrls` is provided.", "default": [ "software engineer" ], "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "Search locations, e.g. \"Sydney\", \"United States\", \"London\". Each keyword is paired with each location. Leave empty to search without a location filter.", "default": [], "items": { "type": "string" } }, "jobUrls": { "title": "LinkedIn job URLs", "type": "array", "description": "Direct public LinkedIn job URLs to inspect for visible posters / hiring-team data. Processed before keyword search. Invalid URLs are skipped and counted in the run summary.", "default": [], "items": { "type": "string" } }, "maxResults": { "title": "Max saved leads", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of saved unique recruiter / job-poster lead rows across the whole run. Range 1-10000.", "default": 100 }, "maxJobsToInspect": { "title": "Max jobs to inspect", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Safety cap on job detail pages opened to look for poster / hiring-team data. Should normally be greater than or equal to Max saved leads.", "default": 250 }, "companyNames": { "title": "Company names filter", "type": "array", "description": "Keep only leads whose company (lead, matched, or job company) contains one of these terms (case-insensitive). Leave empty to keep all.", "default": [], "items": { "type": "string" } }, "titleKeywords": { "title": "Lead title keywords", "type": "array", "description": "Filter person rows by visible title, e.g. \"recruiter\", \"talent acquisition\", \"hiring manager\". In `recruiter_search` / `combined` modes these also seed the recruiter-style searches; leave empty to use a sensible default recruiter title set.", "default": [], "items": { "type": "string" } }, "countryCode": { "title": "Country code", "type": "string", "description": "Optional ISO-like country code (`us`, `au`, `gb`, `ca`) used to localize searches where the public LinkedIn URL supports it. Unknown values are ignored.", "default": "" }, "datePosted": { "title": "Date posted", "enum": [ "any", "past_24h", "past_week", "past_month" ], "type": "string", "description": "Filter jobs by posting recency. Applied at the LinkedIn source where stable.", "default": "any" }, "remoteFilter": { "title": "Workplace type", "enum": [ "any", "remote", "hybrid", "onsite" ], "type": "string", "description": "Filter by work arrangement where visible. Applied at the LinkedIn source where stable; otherwise post-filtered.", "default": "any" }, "includeJobContext": { "title": "Include matched-job fields", "type": "boolean", "description": "Include the matched-job fields (job_title, job_company, job_location, job_url, job_id, posted date, employment type, workplace type, applicants count) alongside each person row. Turn off for a leaner person-only export.", "default": true }, "deduplicate": { "title": "Deduplicate leads", "type": "boolean", "description": "Remove duplicate people across jobs and searches using profile URL / profile ID and normalized name + company. Recommended ON so you are not charged for duplicates.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy configuration. Defaults to Apify Proxy enabled. Apify Residential is NOT supported and will fail the run at startup; if you need residential routing, supply your own provider via Custom proxy URLs (proxyUrls).", "default": { "useApifyProxy": true } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```