# Ai Job Finder (`stefanie-rink/ai-job-finder`) Actor

Give a prompt or a CV and find jobs according to you

- **URL**: https://apify.com/stefanie-rink/ai-job-finder.md
- **Developed by:** [Steafanie Braid](https://apify.com/stefanie-rink) (community)
- **Categories:** Jobs, AI, Open source
- **Stats:** 155 total users, 3 monthly users, 94.3% runs succeeded, 2 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## AI Job Finder

> **⚠️ IMPORTANT**: This Actor requires the LinkedIn Jobs Scraper to function properly. You must rent it from <https://apify.com/bebity/linkedin-jobs-scraper> before using this Actor.

An Apify Actor that uses AI to analyze your CV and/or job search prompt to find relevant jobs on LinkedIn.

### Overview

This Actor combines the power of Large Language Models (LLMs) with LinkedIn job scraping to provide highly relevant job matches. It works by:

1. Reading your CV and/or job search prompt
2. Using AI (OpenAI, Claude, or Gemini) to extract key job search parameters
3. Searching LinkedIn for matching jobs using these parameters
4. Returning detailed job listings that match your profile and preferences

### Features

- **CV Analysis**: Upload your CV/resume and the Actor will analyze it to understand your experience, skills, and career level
- **Natural Language Search**: Describe the job you're looking for in plain language
- **Multiple AI Providers**: Choose between OpenAI, Claude, or Gemini for analysis
- **Detailed Job Listings**: Get complete job details including description, requirements, company information, and application links

### Requirements

- An Apify account
- The LinkedIn Jobs Scraper Actor rented from [apify.com/bebity/linkedin-jobs-scraper](https://apify.com/bebity/linkedin-jobs-scraper)
- An Apify API key
- At least one API key for an LLM provider (OpenAI, Claude, or Gemini)

### Input Parameters

| Parameter | Type | Description |
|-----------|------|-------------|
| `cv` | File | Your CV/resume in PDF, DOCX, or TXT format (optional if prompt is provided) |
| `prompt` | Text | Natural language description of the job you're looking for (optional if CV is provided) |
| `llm_settings` | Object | Configure which LLM provider and model to use |
| `api_keys` | Object | API keys for the LLM providers |
| `apify_api_key` | String | Your Apify API key for calling the LinkedIn Jobs Scraper |
| `linkedin_search_params` | Object | Manual override for LinkedIn search parameters (optional) |
| `proxy` | Object | Proxy configuration for LinkedIn scraping |

#### LLM Settings

```json
{
  "provider": "openai", // One of: "openai", "claude", "gemini"
  "model": "gpt-4o"     // Model to use, depends on the provider
}
```

#### API Keys

```json
{
  "openai": "sk-...",  // OpenAI API key
  "claude": "sk-...",  // Claude API key (if using Claude)
  "gemini": "..."      // Gemini API key (if using Gemini)
}
```

### Example Usage

#### With CV Only

Upload your CV and the Actor will analyze it to find matching jobs based on your experience and skills.

#### With Prompt Only

```
I'm looking for remote Senior Python Developer roles at tech startups, preferably in the AI field. I have 5 years of experience and am looking for full-time positions posted in the last week.
```

#### With Both CV and Prompt

Combining both inputs provides the most accurate results. The Actor will extract information from your CV and use the prompt to refine the search criteria.

### Output

The Actor returns an array of job listings with detailed information about each job, including:

- Job title and company
- Location and work arrangement (remote, on-site, hybrid)
- Full job description
- Application link
- Posting date
- Company details
- Salary information (when available)

### How It Works

1. **Input Validation**: Ensures at least one of CV or prompt is provided
2. **CV Processing**: If a CV is provided, it's analyzed by the selected LLM to extract relevant information
3. **Prompt Processing**: If a prompt is provided, it's analyzed to extract search parameters
4. **Parameter Consolidation**: Parameters from both sources are combined (with prompt taking precedence)
5. **LinkedIn Scraping**: The extracted parameters are used to search for jobs on LinkedIn
6. **Results**: Matching job listings are returned

### Supported LinkedIn Search Parameters

| Parameter | Description | Format |
|-----------|-------------|--------|
| `title` | Job title to search for | String |
| `location` | Geographic location (defaults to "United States" if not specified) | String |
| `experienceLevel` | Experience level | "1" (Internship) to "5" (Director) |
| `workType` | Work arrangement | "1" (On-Site), "2" (Remote), "3" (Hybrid) |
| `contractType` | Employment type | "F" (Full-Time), "P" (Part-Time), etc. |
| `publishedAt` | Time frame | "r86400" (24h), "r604800" (week), "r2592000" (month) |
| `companyName` | List of companies | Array of strings |
| `companyId` | List of LinkedIn company IDs | Array of strings |
| `rows` | Number of results to return | Integer |

# Actor input Schema

## `cv` (type: `object`):

Upload your CV/resume (PDF, DOCX, TXT formats supported) as an object with contentType and buffer properties

## `prompt` (type: `string`):

Describe the job you're looking for (e.g., 'Senior Python Developer in New York')

## `llm_settings` (type: `object`):

Configure which LLM provider to use

## `api_keys` (type: `object`):

API keys for LLM providers (optional - defaults to environment variables)

## `linkedin_search_params` (type: `object`):

Override specific LinkedIn search parameters

## `proxy` (type: `object`):

Configure Apify proxy for LinkedIn scraping

## Actor input object example

```json
{
  "cv": {
    "contentType": "",
    "buffer": ""
  },
  "prompt": "I'm looking for remote senior software engineering roles in AI companies. I have 5 years of experience with Python and machine learning.",
  "llm_settings": {
    "provider": "gemini",
    "model": "gemini-1.5-pro"
  },
  "api_keys": {
    "openai": "",
    "claude": "",
    "gemini": ""
  },
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "cv": {
        "contentType": "",
        "buffer": ""
    },
    "llm_settings": {
        "provider": "gemini",
        "model": "gemini-1.5-pro"
    },
    "api_keys": {
        "openai": "",
        "claude": "",
        "gemini": ""
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("stefanie-rink/ai-job-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "cv": {
        "contentType": "",
        "buffer": "",
    },
    "llm_settings": {
        "provider": "gemini",
        "model": "gemini-1.5-pro",
    },
    "api_keys": {
        "openai": "",
        "claude": "",
        "gemini": "",
    },
}

# Run the Actor and wait for it to finish
run = client.actor("stefanie-rink/ai-job-finder").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "cv": {
    "contentType": "",
    "buffer": ""
  },
  "llm_settings": {
    "provider": "gemini",
    "model": "gemini-1.5-pro"
  },
  "api_keys": {
    "openai": "",
    "claude": "",
    "gemini": ""
  }
}' |
apify call stefanie-rink/ai-job-finder --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,stefanie-rink/ai-job-finder"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ANSWJ5bUWJws7Dz5H/builds/RAyJQLdnTaFAvDsK6/openapi.json