# LinkedIn Jobs Scraper – No Login (`datascraperes/linkedin-jobs-scraper`) Actor Scrape public LinkedIn jobs by keyword and location. Filter by date, job type, experience, workplace, Easy Apply, or under 10 applicants. Extract company, salary, applicant count, seniority, employment type, description, and job URL when available. - **URL**: https://apify.com/datascraperes/linkedin-jobs-scraper.md - **Developed by:** [DataScraperES](https://apify.com/datascraperes) (community) - **Categories:** Jobs, Automation, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $0.60 / 1,000 scraped jobs This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 Jobs Scraper – No Login [Run the Actor on Apify](https://apify.com/datascraperes/linkedin-jobs-scraper) Search public LinkedIn job listings by keyword and location and export one structured dataset item per unique job. Use filters for publication date, employment type, experience level, workplace type, Easy Apply, and jobs shown with fewer than 10 applicants. The Actor can return lightweight search-card data or visit each public job page to add the full description, applicant text, seniority, employment type, job function, and industries. ### What can you use it for? - Build or refresh a job board. - Monitor which roles competitors are hiring for. - Track remote, hybrid, or on-site demand by market. - Analyze salary, seniority, and employment-type trends. - Find newly published roles for recruiting or job-search workflows. - Send fresh job listings to a spreadsheet, database, CRM, webhook, or automation. ### What data can you collect? Each result can contain: - LinkedIn job ID and public job URL - Job title and location - Company name, LinkedIn company page, and logo - Publication date and relative posting age - Salary text when LinkedIn displays it - Applicant text when publicly available - Full public job description - Seniority level and employment type - Job function and industries - The keyword and location that discovered the job - Detail status and request metadata for transparent quality checks Field availability depends on what LinkedIn publishes for each job. Missing optional values are returned as `null`; the Actor does not invent unavailable data. ### Pricing The Actor costs **$0.0006 per unique job written to the dataset**, equivalent to **$0.60 per 1,000 jobs**. | Dataset results | Price | |---:|---:| | 25 | $0.015 | | 100 | $0.06 | | 500 | $0.30 | | 1,000 | $0.60 | | 5,000 | $3.00 | There is **no Actor start fee**. You are not charged for duplicate jobs, retries, failed search requests, or searches that produce no dataset items. A valid job card is still a billable result when optional detail fields cannot be recovered. In that case, the item is saved with `status: "partial"` and a `detailStatus` explaining which optional detail request failed. ### Quick start 1. Open the Actor's **Input** tab. 2. Add one or more job-search keywords. 3. Enter a location such as `Spain`, `Madrid`, `United States`, or `Worldwide`. 4. Choose the maximum number of jobs for each keyword. 5. Add optional filters. 6. Keep **Fetch full job details** enabled when you need descriptions and job criteria. 7. Click **Start**. 8. Open the run's **Dataset** to preview or download the results. No proxy, LinkedIn account, or cookie configuration is required from the user. ### Input reference | Field | Type | Required | Default | Description | |---|---|:---:|---|---| | `keywords` | string array | Yes | — | One independent search per keyword. Accepts 1–50 unique values. | | `location` | string | Yes | — | Free-text location understood by LinkedIn, such as a country, city, region, or `Worldwide`. | | `geoId` | string | No | Empty | Numeric LinkedIn geographic ID. Use it when you need more exact geographic targeting. | | `maxItemsPerQuery` | integer | No | `25` | Maximum jobs collected for each keyword, from 1 to 1,000. This is a limit, not a guaranteed result count. | | `datePosted` | string | No | `any` | Publication-age filter: `any`, `past24Hours`, `pastWeek`, or `pastMonth`. | | `jobTypes` | string array | No | All | Filter by full-time, part-time, contract, temporary, volunteer, internship, or other. | | `experienceLevels` | string array | No | All | Filter from internship through executive level. | | `workplaceTypes` | string array | No | All | Filter by on-site, remote, or hybrid work. | | `easyApplyOnly` | boolean | No | `false` | Return only jobs marked with LinkedIn Easy Apply. | | `under10ApplicantsOnly` | boolean | No | `false` | Return only jobs shown as having fewer than 10 applicants. | | `includeJobDetails` | boolean | No | `true` | Fetch each public detail page. Disable it for faster search-card-only collection. | #### Accepted filter values `jobTypes`: ```text fullTime, partTime, contract, temporary, volunteer, internship, other ```` `experienceLevels`: ```text internship, entryLevel, associate, midSenior, director, executive ``` `workplaceTypes`: ```text onSite, remote, hybrid ``` ### Input examples #### Basic job search Collect up to 25 software engineering jobs in Spain with full details: ```json { "keywords": ["software engineer"], "location": "Spain", "maxItemsPerQuery": 25, "includeJobDetails": true } ``` #### Recent remote and hybrid jobs Search two roles and keep jobs posted during the past week: ```json { "keywords": ["data engineer", "analytics engineer"], "location": "Europe", "maxItemsPerQuery": 100, "datePosted": "pastWeek", "jobTypes": ["fullTime", "contract"], "experienceLevels": ["associate", "midSenior"], "workplaceTypes": ["remote", "hybrid"], "includeJobDetails": true } ``` #### Easy Apply with fewer applicants ```json { "keywords": ["SEO specialist"], "location": "Madrid, Spain", "maxItemsPerQuery": 50, "datePosted": "past24Hours", "easyApplyOnly": true, "under10ApplicantsOnly": true, "includeJobDetails": true } ``` #### Faster card-only collection Disable job details when you only need job discovery fields such as title, company, location, posting date, salary text, and URL: ```json { "keywords": ["product manager", "project manager"], "location": "United States", "maxItemsPerQuery": 250, "datePosted": "pastMonth", "includeJobDetails": false } ``` With `includeJobDetails: false`, these fields normally remain `null`: `applicantsText`, `descriptionText`, `seniorityLevel`, `employmentType`, `jobFunction`, and `industries`. ### How limits and duplicates work `maxItemsPerQuery` applies separately to every keyword. For example, three keywords with `maxItemsPerQuery: 100` can inspect up to 300 job cards. The final number can be lower because: - LinkedIn returned fewer matching public jobs. - The selected filters narrowed the search. - The same job appeared under multiple keywords. - A public search page was temporarily unavailable. Jobs are deduplicated by LinkedIn job ID across the entire run. When the same job matches multiple keywords, it is written once and keeps the first `searchKeyword` that discovered it. ### Output example Each dataset item represents one unique LinkedIn job: ```json { "success": true, "status": "success", "searchKeyword": "software engineer", "searchLocation": "Spain", "jobId": "1234567890", "jobUrl": "https://www.linkedin.com/jobs/view/1234567890", "title": "Software Engineer", "companyName": "Example Corp", "companyUrl": "https://www.linkedin.com/company/example", "companyLogoUrl": "https://media.licdn.com/example-logo.png", "location": "Madrid, Spain", "postedDate": "2026-07-12", "postedText": "1 day ago", "salary": null, "applicantsText": "47 applicants", "descriptionText": "Build and maintain reliable services...", "seniorityLevel": "Mid-Senior level", "employmentType": "Full-time", "jobFunction": "Engineering and Information Technology", "industries": "Software Development", "criteria": { "seniorityLevel": "Mid-Senior level", "employmentType": "Full-time" }, "detailStatus": "success", "detailHttpStatus": 200, "detailAttempts": 1, "scrapedAt": "2026-07-13T12:00:00Z" } ``` ### Output field reference | Field | Description | |---|---| | `success` | `true` when all requested data for the item was collected successfully. | | `status` | `success` or `partial`. | | `searchKeyword` | First keyword that discovered the job. | | `searchLocation` | Location supplied in the Actor input. | | `jobId` | LinkedIn's unique job identifier. | | `jobUrl` | Canonical public LinkedIn job URL. | | `title` | Job title. | | `companyName` | Hiring company name. | | `companyUrl` | Public LinkedIn company page when available. | | `companyLogoUrl` | Company logo URL when available. | | `location` | Job location displayed by LinkedIn. | | `postedDate` | Parsed publication date when available. | | `postedText` | Relative posting age, for example `1 day ago`. | | `salary` | Public salary text when shown on the search card. | | `applicantsText` | Public applicant text from the job detail page. | | `descriptionText` | Full public job description as plain text. | | `seniorityLevel` | LinkedIn seniority criterion. | | `employmentType` | Full-time, part-time, contract, internship, or another published type. | | `jobFunction` | Published job function. | | `industries` | Published industry information. | | `criteria` | All parsed job criteria as key-value pairs. | | `detailStatus` | Result of the optional job-detail request. | | `detailHttpStatus` | HTTP status returned by the detail request when available. | | `detailAttempts` | Number of attempts used for the detail request. | | `scrapedAt` | UTC timestamp when the item was created. | The external application destination is not guaranteed because LinkedIn does not consistently expose it in public job responses. `jobUrl` always points to the public LinkedIn job posting. ### Understanding result status - `status: "success"`: the requested result was collected successfully. - `status: "partial"`: the valid search card was saved, but the optional detail page did not recover after bounded retries. - `detailStatus: "not_requested"`: `includeJobDetails` was disabled. - `detailStatus: "success"`: the public detail page was fetched and parsed. - Other `detailStatus` values identify temporary blocking, rate limiting, network failure, an unavailable job, an upstream error, or an unrecognized response. Partial results retain the core job fields and can still be useful in job-discovery pipelines. ### Downloading and integrating results Open the run's **Dataset** tab to preview the results. Apify can export the dataset as JSON, JSONL, CSV, Excel, XML, RSS, or an HTML table. The Actor output also provides: - `results`: API URL for the default dataset items. - `summary`: API URL for the non-sensitive `SUMMARY` record. The summary contains query counts, output totals, successful and partial item counts, skipped duplicates, pagination totals, retries, and total duration. ### Run with the Apify API Replace `YOUR_APIFY_TOKEN` and the example input values as needed. #### cURL Run the Actor synchronously and return its dataset items: ```bash curl -X POST \ "https://api.apify.com/v2/acts/datascraperes~linkedin-jobs-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "keywords": ["software engineer"], "location": "Spain", "maxItemsPerQuery": 25, "datePosted": "pastWeek", "includeJobDetails": true }' ``` #### Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("datascraperes/linkedin-jobs-scraper").call( run_input={ "keywords": ["software engineer", "data engineer"], "location": "Spain", "maxItemsPerQuery": 50, "datePosted": "pastWeek", "workplaceTypes": ["remote", "hybrid"], "includeJobDetails": True, } ) for job in client.dataset(run["defaultDatasetId"]).iterate_items(): print(job["title"], job["companyName"], job["jobUrl"]) ``` #### JavaScript ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('datascraperes/linkedin-jobs-scraper').call({ keywords: ['software engineer', 'data engineer'], location: 'Spain', maxItemsPerQuery: 50, datePosted: 'pastWeek', workplaceTypes: ['remote', 'hybrid'], includeJobDetails: true, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); for (const job of items) { console.log(job.title, job.companyName, job.jobUrl); } ``` #### Apify CLI Save your input as `input.json`, then run: ```bash apify actors call datascraperes/linkedin-jobs-scraper \ --input-file input.json \ --output-dataset ``` The Actor's **API** tab in Apify Store also generates ready-to-run examples for the current Actor version. ### Scheduling recurring searches Use an Apify Schedule when you need fresh jobs every hour, day, or week: 1. Save the desired Actor input as a Task. 2. Create a Schedule for that Task. 3. Use `past24Hours` or `pastWeek` to focus on fresh listings. 4. Send completed-run results through a webhook or an Apify integration. 5. Deduplicate across scheduled runs in your destination using `jobId`. Deduplication inside the Actor applies to one run and its resurrection. Separate scheduled runs use separate datasets, so persistent monitoring systems should keep previously seen `jobId` values in their own database or automation. ### Reliability and resuming runs The Actor uses controlled concurrency, request pacing, retries with backoff, fresh proxy sessions after transient failures, pagination safety limits, and deduplication by job ID. If an Apify run is resurrected with the same input, it restores its saved progress and avoids writing completed jobs again. Changing the input creates a new progress fingerprint and starts a new search. ### Tips for better results - Use specific keywords such as `senior React developer` instead of a broad term such as `developer`. - Run separate searches for important title variations. - Use a precise location or `geoId` when the free-text location is ambiguous. - Use `past24Hours` or `pastWeek` for recurring monitoring. - Disable `includeJobDetails` when you only need discovery fields and want a faster run. - Keep full details enabled when descriptions, applicant text, seniority, job function, or industries are required. - Remember that salary and applicant information are not published for every job. ### Troubleshooting | Situation | Explanation and action | |---|---| | No results | Broaden the keyword, location, or date filters and confirm that the same search has public LinkedIn results. | | Fewer jobs than requested | `maxItemsPerQuery` is a maximum. LinkedIn may expose fewer jobs, and duplicates are removed. | | Some fields are `null` | The employer or LinkedIn did not publish those optional fields, or full details were disabled. | | Results have `status: "partial"` | The core job card is valid, but an optional detail request failed after retries. Check `detailStatus`. | | The run reports failed queries | Review the `SUMMARY` record. Retry later or split a large multi-keyword input into smaller runs. | | Results repeat across scheduled runs | Deduplicate in your destination using `jobId`; each scheduled run has its own dataset. | | Location is inaccurate | Use the full location name or provide a numeric LinkedIn `geoId`. | | The input is rejected | Check the exact enum values in the Input reference and keep `maxItemsPerQuery` between 1 and 1,000. | ### Limits and important notes - A maximum of 50 unique keywords is accepted per run. - Each keyword can request up to 1,000 public job cards. - The maximum is not guaranteed because it depends on public availability and selected filters. - Public page structure and field availability can change. - Closed or removed jobs can disappear between search and detail retrieval. - Salary, applicants, company page, logo, and job criteria are best-effort fields. - This Actor collects job-posting data, not private LinkedIn account data. ### Local development Clone the repository and create a Python 3.13 virtual environment: ```bash git clone https://github.com/datacrawler-edu/actor-linkedin-jobs.git cd actor-linkedin-jobs python -m venv .venv ``` On Windows PowerShell: ```powershell .\.venv\Scripts\Activate.ps1 python -m pip install -r requirements-dev.txt ``` Run the local quality gate: ```powershell python -m ruff check . python -m ruff format --check . python -m compileall -q src tests python -m pytest -q apify validate-schema ``` Real HTTP validation requires the private proxy URL at runtime. Never store a real credential in `.env`, source files, fixtures, logs, or Git history. See [`docs/PRODUCTION_RUNBOOK.md`](docs/PRODUCTION_RUNBOOK.md) for the release procedure. ### Project documentation - [`docs/PRODUCTION_RUNBOOK.md`](docs/PRODUCTION_RUNBOOK.md): secrets, release gate, deployment, monetization, and incident signals. - [`docs/VALIDATION_2026-07-13.md`](docs/VALIDATION_2026-07-13.md): local and remote validation evidence. - [`docs/HTTP_RESEARCH_2026-07-13.md`](docs/HTTP_RESEARCH_2026-07-13.md): researched public endpoint and parser contract. - [`CHANGELOG.md`](CHANGELOG.md): release history. ### Responsible use This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedIn is a registered trademark of its owner. Use the data responsibly and ensure that your use complies with applicable laws, data-protection requirements, and the terms that apply to the source and your intended use case. # Actor input Schema ## `keywords` (type: `array`): One job search per item, for example Software Engineer or SEO Specialist. Blank values and duplicate terms are removed. ## `location` (type: `string`): Location text accepted by LinkedIn Jobs, for example Spain, Madrid, New York, or Worldwide. ## `geoId` (type: `string`): Optional numeric LinkedIn geographic ID for an exact location. Leave empty to use the location text only. ## `maxItemsPerQuery` (type: `integer`): Maximum number of job cards collected for each keyword. Duplicate job IDs across searches are output only once. ## `datePosted` (type: `string`): Restrict results by publication age. ## `jobTypes` (type: `array`): Optional employment-type filters. Leave empty to include all types. ## `experienceLevels` (type: `array`): Optional seniority filters. Leave empty to include all levels. ## `workplaceTypes` (type: `array`): Optional workplace filters. Leave empty to include all workplace types. ## `easyApplyOnly` (type: `boolean`): Return only postings marked with LinkedIn Easy Apply. ## `under10ApplicantsOnly` (type: `boolean`): Return only postings shown by LinkedIn as having fewer than 10 applicants. ## `includeJobDetails` (type: `boolean`): Fetch each public detail page to add the description, applicant text, seniority, employment type, function, and industries. Disable for a faster card-only run. ## Actor input object example ```json { "keywords": [ "software engineer" ], "location": "Spain", "maxItemsPerQuery": 25, "datePosted": "any", "jobTypes": [], "experienceLevels": [], "workplaceTypes": [], "easyApplyOnly": false, "under10ApplicantsOnly": false, "includeJobDetails": true } ``` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (type: `string`): No description # 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" ], "location": "Spain" }; // Run the Actor and wait for it to finish const run = await client.actor("datascraperes/linkedin-jobs-scraper").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"], "location": "Spain", } # Run the Actor and wait for it to finish run = client.actor("datascraperes/linkedin-jobs-scraper").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" ], "location": "Spain" }' | apify call datascraperes/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datascraperes/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper – No Login", "description": "Scrape public LinkedIn jobs by keyword and location. Filter by date, job type, experience, workplace, Easy Apply, or under 10 applicants. Extract company, salary, applicant count, seniority, employment type, description, and job URL when available.", "version": "0.1", "x-build-id": "YIu9kvifEpjSBYhJO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datascraperes~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datascraperes-linkedin-jobs-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datascraperes~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-datascraperes-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datascraperes~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-datascraperes-linkedin-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "location" ], "properties": { "keywords": { "title": "Search keywords", "minItems": 1, "maxItems": 50, "type": "array", "description": "One job search per item, for example Software Engineer or SEO Specialist. Blank values and duplicate terms are removed.", "items": { "type": "string", "minLength": 1, "maxLength": 200 } }, "location": { "title": "Location", "minLength": 1, "maxLength": 200, "type": "string", "description": "Location text accepted by LinkedIn Jobs, for example Spain, Madrid, New York, or Worldwide." }, "geoId": { "title": "LinkedIn geo ID (optional)", "pattern": "^[0-9]{1,20}$", "type": "string", "description": "Optional numeric LinkedIn geographic ID for an exact location. Leave empty to use the location text only." }, "maxItemsPerQuery": { "title": "Maximum jobs per search", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of job cards collected for each keyword. Duplicate job IDs across searches are output only once.", "default": 25 }, "datePosted": { "title": "Date posted", "enum": [ "any", "past24Hours", "pastWeek", "pastMonth" ], "type": "string", "description": "Restrict results by publication age.", "default": "any" }, "jobTypes": { "title": "Job types", "uniqueItems": true, "type": "array", "description": "Optional employment-type filters. Leave empty to include all types.", "items": { "type": "string", "enum": [ "fullTime", "partTime", "contract", "temporary", "volunteer", "internship", "other" ], "enumTitles": [ "Full-time", "Part-time", "Contract", "Temporary", "Volunteer", "Internship", "Other" ] }, "default": [] }, "experienceLevels": { "title": "Experience levels", "uniqueItems": true, "type": "array", "description": "Optional seniority filters. Leave empty to include all levels.", "items": { "type": "string", "enum": [ "internship", "entryLevel", "associate", "midSenior", "director", "executive" ], "enumTitles": [ "Internship", "Entry level", "Associate", "Mid-Senior level", "Director", "Executive" ] }, "default": [] }, "workplaceTypes": { "title": "Workplace types", "uniqueItems": true, "type": "array", "description": "Optional workplace filters. Leave empty to include all workplace types.", "items": { "type": "string", "enum": [ "onSite", "remote", "hybrid" ], "enumTitles": [ "On-site", "Remote", "Hybrid" ] }, "default": [] }, "easyApplyOnly": { "title": "Easy Apply only", "type": "boolean", "description": "Return only postings marked with LinkedIn Easy Apply.", "default": false }, "under10ApplicantsOnly": { "title": "Under 10 applicants only", "type": "boolean", "description": "Return only postings shown by LinkedIn as having fewer than 10 applicants.", "default": false }, "includeJobDetails": { "title": "Fetch full job details", "type": "boolean", "description": "Fetch each public detail page to add the description, applicant text, seniority, employment type, function, and industries. Disable for a faster card-only run.", "default": 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 } } } } } } } } } } ```