# LinkedIn Jobs Search with Detail and Hiring Team Cookie-less ✅ (`datamagnet/linkedin-job-search-enricher`) Actor Search LinkedIn jobs by keyword and filters, then optionally enrich selected listings with job details and hiring team information in one run. Get raw job search results you can review, export, and use for recruiting, research, or prospecting. - **URL**: https://apify.com/datamagnet/linkedin-job-search-enricher.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** Jobs, Automation - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Job Search Enricher — LinkedIn Jobs Detail with Hiring Team Scraper Find LinkedIn jobs faster and enrich the listings you care about in one simple run. Job Search Enricher helps recruiters, job seekers, market researchers, and sales teams collect LinkedIn job listings, then optionally add detailed job information and hiring team data for selected roles. Use it to search by keyword, location, seniority, job type, salary, and more, then review the raw results or export them for analysis. It is a practical LinkedIn jobs data collector for anyone who wants better job intelligence without manual copy-paste. ### Key Features - **Extract** LinkedIn job listings based on the keyword and filters you choose. - **Collect** only the number of results you need, from a small shortlist to a larger research set. - **Filter** jobs by location, date posted, experience level, job type, work mode, salary, company, function, industry, and title. - **Enrich** selected jobs with detailed job information so you can review responsibilities, company context, and application options. - **Reveal** hiring team information for specific jobs or job URLs to help you identify the people behind the posting. - **Export** raw results for easy review in spreadsheets, reports, or internal workflows. - **Save** time by combining search and enrichment in one run instead of checking listings one by one. ### Use Cases #### Recruiting and Talent Sourcing Recruiters use this actor to find active LinkedIn job listings that match a target role, seniority level, or location. The returned fields such as title, company name, location, posted date, work mode, and hiring team help recruiters understand the market and identify who is hiring right now. #### Job Market Research Analysts and founders can collect job listings to track hiring trends across companies, industries, and functions. Fields like company, job functions, industries, salary, and date posted make it easier to compare demand across roles and spot changes in the market. #### Sales Prospecting Sales teams can search for companies that are actively hiring for specific roles, which often signals growth, new projects, or budget availability. The company details, job title, and hiring team information help sales reps prioritize outreach and personalize their messaging. #### Competitive Intelligence Businesses can monitor what competitors are hiring for and how they describe open roles. By reviewing job descriptions, work mode, experience level, and company information, teams can better understand product direction, team structure, and expansion plans. #### Candidate Research Job seekers can use this actor to build a focused list of opportunities instead of browsing endlessly. The output shows the job title, company, location, application method, and detailed description, helping candidates decide which roles are worth applying to. #### Hiring Team Discovery When you already know the job ID or job URL, you can fetch hiring team information to see who is connected to the role. This is useful for networking, referral outreach, and understanding which people may be involved in the hiring process. ### Input | Field | Type | Required | Description | Example | |---|---|---:|---|---| | id | string | No | A single job ID for getting detailed information about one job. | 4090994054 | | sort | string | No | Choose whether to show the most relevant jobs or the newest jobs first. | mostRelevant | | jobIds | array | No | One or more job IDs for getting detailed information about selected jobs. | ["4090994054", "4090994055", "4090994056"] | | salary | string | No | Show only jobs that meet or exceed a chosen salary level. | 40k+ | | jobType | string | No | Limit results to a specific type of employment, such as full-time or internship. | fullTime | | job_ids | array | No | One or more job IDs for getting detailed information about selected jobs. | ["4090994054", "4090994055", "4090994056"] | | keywords | string | Yes | The main word or phrase for the jobs you want to find. | golang | | titleIds | string | No | Comma-separated title IDs to narrow results to specific job titles. | 10,20 | | companyIds | string | No | Comma-separated company IDs to limit results to specific employers. | 1441,162479 | | datePosted | string | No | Show jobs posted within a chosen time range. | anyTime | | hiringTeam | array | No | One or more job IDs or job URLs for getting hiring team information. | ["4090994054", "https://www.example.com/jobs/4090994054", "4090994055"] | | locationId | string | No | Narrow the search to a specific location by its ID. | 92000000 | | maxResults | integer | No | Set the maximum number of job listings to collect. | 100 | | functionIds | string | No | Comma-separated function IDs to narrow results by role function. | 1,3,5 | | hiring_team | array | No | One or more job IDs or job URLs for getting hiring team information. | ["4090994054", "https://www.example.com/jobs/4090994054", "4090994055"] | | industryIds | string | No | Comma-separated industry IDs to limit results to specific industries. | 4,96 | | max_results | integer | No | Set the maximum number of job listings to collect. | 100 | | onsiteRemote | string | No | Filter jobs by workplace style, such as remote, hybrid, or on-site. | hybrid | | experienceLevel | string | No | Filter jobs by seniority level. | midSeniorLevel | | hiringTeamInputs | array | No | One or more job IDs or job URLs for getting hiring team information. | ["4090994054", "https://www.example.com/jobs/4090994054", "4090994055"] | | hiring_team_inputs | array | No | One or more job IDs or job URLs for getting hiring team information. | ["4090994054", "https://www.example.com/jobs/4090994054", "4090994055"] | ### Output | Field | Type | Description | |---|---|---| | id | string | The job’s unique identifier, useful for tracking or matching the same listing later. | | title | string | The job title shown on the listing. | | url | string | The direct link to the job listing. | | referenceId | string | A reference value that helps identify the listing in the returned data. | | company | object | Basic company information such as name, logo, website, size, and headquarters. | | location | string | The job location as shown in the listing. | | postAt | string | The date and time the job was posted. | | postedTimestamp | number | The posting time in a machine-friendly format for sorting and filtering. | | job_details | object | Extra job information such as description, work mode, application method, and company details. | | hiring_team | object | Hiring team information connected to the job, if available. | ### Sample Output ```json { "id": "4413200050", "title": "Golang Developer", "url": "https://www.linkedin.com/jobs/view/4413200050", "referenceId": "3c1XNJKkq1TH5HD9X5C9uQ==", "company": { "name": "Azberry BV", "logo": "https://media.licdn.com/dms/image/v2/D4D0BAQGHTtoKfIlDLw/company-logo_400_400/B4DZY6ORbLGwAY-/0/1744733542538/azberry_bv_logo?e=1785369600&v=beta&t=nyXEA7hwgeGyR3P_M6t23UbEL3TzdXV33mAo8ClFJn0", "url": "https://www.linkedin.com/company/azberry-bv/life", "staffCountRange": {}, "headquarter": {} }, "location": "Türkiye (Remote)", "postAt": "2026-05-11 23:00:46 +0000 UTC", "postedTimestamp": 1778540446000, "job_details": { "id": "4413200050", "state": "LISTED", "title": "Golang Developer", "description": "About TubiaTubia builds high-performance AdTech infrastructure that handles hundreds of thousands of real-time bid requests every second. We value curiosity, rapid learning, and clean engineering practices in a remote-first, outcome-driven culture....", "url": "https://www.linkedin.com/jobs/view/4413200050/", "applyMethod": { "companyApplyUrl": "", "easyApplyUrl": "" }, "company": { "id": 100755618, "name": "Azberry BV", "universalName": "azberry-bv", "description": "Unleash the Power of Adtech with Azberry BV...", "logo": "https://media.licdn.com/dms/image/v2/D4D0BAQGHTtoKfIlDLw/company-logo_400_400/B4DZY6ORbLGwAY-/0/1744733542538/azberry_bv_logo?e=1785369600&v=beta&t=nyXEA7hwgeGyR3P_M6t23UbEL3TzdXV33mAo8ClFJn0", "url": "https://www.linkedin.com/company/azberry-bv", "followerCount": 6388, "staffCount": 15, "staffCountRange": { "start": 51, "end": 200 }, "industries": [ "Software Development" ], "headquarter": { "geographicArea": "North Holland", "country": "NL", "city": "Schiphol-Rijk", "postalCode": "1119 RB", "line1": "Beechavenue 137" } }, "contentLanguage": { "code": "EN", "name": "English" }, "location": "Türkiye", "type": "Full-time", "workRemoteAllowed": true, "workPlace": "Remote", "expireAt": 1794092446000, "jobFunctions": [ "ENG", "IT" ], "industries": [ 6 ], "listedAt": 1778540446000, "listedAtDate": "2026-05-11 23:00:46 +0000 UTC", "originalListedAt": 1778540446000, "originalListedDate": "2026-05-11 23:00:46 +0000 UTC" }, "hiring_team": { "items": null } } ```` ### How It Works 1. You enter a keyword for the jobs you want to find, such as a role, skill, or job title. 2. You optionally narrow the search with filters like location, date posted, experience level, job type, work mode, salary, company, industry, or title. 3. The actor collects matching LinkedIn job listings and keeps going until it reaches your chosen limit or runs out of results. 4. If you provide job IDs, it adds detailed job information for those listings. 5. If you provide job IDs or job URLs for hiring team lookup, it adds hiring team information for those jobs as well. ### Getting Started Getting started is simple and does not require coding. Click **Try for free**, enter your search keyword, choose any filters you want, and click **Run**. In a few moments, you will have structured results you can review or export. Your data is available in JSON, CSV, or Excel, so it is easy to share with your team or use in spreadsheets. ### Frequently Asked Questions #### Do I need technical skills? No. This actor is designed for business users, recruiters, analysts, and job seekers who want results without manual searching. You only need to enter the job keyword and any filters you care about. #### How fast does it run? It usually runs quickly for small searches and may take longer when you request more results or add extra enrichment for specific jobs. The total time depends on how many listings you want and whether you also ask for job details or hiring team information. #### What format is the output? You can export the results in JSON, CSV, or Excel. That makes it easy to review the data in a spreadsheet, share it with colleagues, or use it in reporting workflows. #### Is it legal to use? You should always use job data responsibly and in line with the rules that apply to your use case, your location, and the platform’s terms. This actor is intended for legitimate research, recruiting, and business analysis, and you should avoid any use that violates applicable policies or laws. #### Can I schedule it to run automatically? Yes, you can run it whenever you need fresh results, including on a recurring schedule if your workflow requires regular updates. That is useful for tracking new job postings, monitoring competitors, or keeping a live hiring list up to date. # Actor input Schema ## `keywords` (type: `string`): Search terms for the jobs you want to find. ## `get_hiring_team` (type: `boolean`): For the jobs collected via search, for all the jobs also fetch the hiring team data. ## `get_job_details` (type: `boolean`): For the jobs collected via search, fetches detailed job information ## `onsiteRemote` (type: `string`): Filter jobs by workplace arrangement. ## `experienceLevel` (type: `string`): Filter jobs by seniority level. ## `datePosted` (type: `string`): Show jobs posted within this time range. ## `sort` (type: `string`): Choose how to sort the results. ## `salary` (type: `string`): Filter jobs by minimum salary range. ## `jobType` (type: `string`): Filter jobs by employment type. ## `titleIds` (type: `string`): Comma-separated title IDs to filter jobs by job title. ## `companyIds` (type: `string`): Comma-separated company IDs to limit the search to specific companies. ## `locationId` (type: `string`): Location ID to narrow the search to a specific place. ## `functionIds` (type: `string`): Comma-separated function IDs to filter jobs by role function. ## `industryIds` (type: `string`): Comma-separated industry IDs to filter jobs by industry. ## `max_results` (type: `integer`): Maximum number of search results to return. ## Actor input object example ```json { "keywords": "golang", "datePosted": "anyTime", "sort": "mostRelevant", "locationId": "92000000", "max_results": 10 } ``` # 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": "golang" }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/linkedin-job-search-enricher").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": "golang" } # Run the Actor and wait for it to finish run = client.actor("datamagnet/linkedin-job-search-enricher").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": "golang" }' | apify call datamagnet/linkedin-job-search-enricher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/linkedin-job-search-enricher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Search with Detail and Hiring Team Cookie-less ✅", "description": "Search LinkedIn jobs by keyword and filters, then optionally enrich selected listings with job details and hiring team information in one run. Get raw job search results you can review, export, and use for recruiting, research, or prospecting.", "version": "0.0", "x-build-id": "Vvau89fHnbPjUa1Ib" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~linkedin-job-search-enricher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-linkedin-job-search-enricher", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~linkedin-job-search-enricher/runs": { "post": { "operationId": "runs-sync-datamagnet-linkedin-job-search-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~linkedin-job-search-enricher/run-sync": { "post": { "operationId": "run-sync-datamagnet-linkedin-job-search-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "string", "description": "Search terms for the jobs you want to find." }, "get_hiring_team": { "title": "Fetch hiring team information", "type": "boolean", "description": "For the jobs collected via search, for all the jobs also fetch the hiring team data." }, "get_job_details": { "title": "Fetch detailed job information, for the all searched job", "type": "boolean", "description": "For the jobs collected via search, fetches detailed job information" }, "onsiteRemote": { "title": "Work Mode", "enum": [ "onSite", "remote", "hybrid" ], "type": "string", "description": "Filter jobs by workplace arrangement." }, "experienceLevel": { "title": "Experience Level", "enum": [ "internship", "associate", "director", "entryLevel", "midSeniorLevel", "executive" ], "type": "string", "description": "Filter jobs by seniority level." }, "datePosted": { "title": "Date Posted", "enum": [ "anyTime", "pastMonth", "pastWeek", "past24Hours" ], "type": "string", "description": "Show jobs posted within this time range." }, "sort": { "title": "Sort", "enum": [ "mostRelevant", "mostRecent" ], "type": "string", "description": "Choose how to sort the results." }, "salary": { "title": "Salary", "enum": [ "40k+", "60k+", "80k+", "100k+", "120k+", "140k+", "160k+", "180k+", "200k+" ], "type": "string", "description": "Filter jobs by minimum salary range." }, "jobType": { "title": "Job Type", "enum": [ "fullTime", "partTime", "contract", "internship" ], "type": "string", "description": "Filter jobs by employment type." }, "titleIds": { "title": "Title IDs", "type": "string", "description": "Comma-separated title IDs to filter jobs by job title." }, "companyIds": { "title": "Company IDs", "type": "string", "description": "Comma-separated company IDs to limit the search to specific companies." }, "locationId": { "title": "Location ID", "type": "string", "description": "Location ID to narrow the search to a specific place." }, "functionIds": { "title": "Function IDs", "type": "string", "description": "Comma-separated function IDs to filter jobs by role function." }, "industryIds": { "title": "Industry IDs", "type": "string", "description": "Comma-separated industry IDs to filter jobs by industry." }, "max_results": { "title": "Max Results", "maximum": 10, "type": "integer", "description": "Maximum number of search results to return.", "default": 10 } } }, "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 } } } } } } } } } } ```