# Global Internship Finder (`tuhin/global-internship-finder`) Actor

Discover fresh internships across Europe and beyond, collected automatically from public job boards and company career pages. Normalized, deduplicated, and relevance-scored.

- **URL**: https://apify.com/tuhin/global-internship-finder.md
- **Developed by:** [Tuhin](https://apify.com/tuhin) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 80.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 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.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Global Internship Finder

**Discover fresh internships across Europe and beyond — collected automatically from public job boards and company career pages, then normalized, deduplicated, and relevance-scored.**

Select countries, cities, roles, languages, and remote/hybrid/on-site preference, and get a clean, ranked dataset of *validated internships* — ready to power an internship search website.

> **Responsible use:** Uses only *public / authorized* sources and official-style JSON endpoints (The Muse API, Arbeitnow API, Remotive API, and public Greenhouse/Lever ATS boards). It does **not** log in, scrape LinkedIn/Indeed, bypass CAPTCHAs, or access private/paywalled data. LinkedIn & Indeed are intentionally excluded because their terms/anti-bot controls prohibit it.

***

### Sources (modular — add more by dropping a connector in `src/sources/`)

| Source | Coverage | Notes |
|--------|----------|-------|
| **The Muse** | Global, explicit `Internship` level | Best structured internship source (category + location) |
| **Arbeitnow** | Europe (esp. Germany) | Public EU job board |
| **Remotive** | Remote | Remote internships |
| **Greenhouse** | Per company | Public ATS boards — exposes deadlines & education |
| **Lever** | Per company | Public ATS boards |

### Input (all optional — run with none for a broad EU sweep)

| Field | Description |
|-------|-------------|
| `countries` / `cities` | Location filters, e.g. `["Germany","Netherlands"]`, `["Berlin"]`. |
| `categories` | Roles, e.g. `["Software Engineering","Data Science","Finance"]` (also queries The Muse). |
| `keywords` | Must appear in title/description. |
| `locationType` | `any` / `remote` / `hybrid` / `onsite`. |
| `languages` | Required languages, e.g. `["English","German"]`. |
| `sources` | Which connectors to run. Default `["themuse","arbeitnow","remotive"]`. |
| `greenhouseCompanies` / `leverCompanies` | Company board tokens for ATS sources. |
| `maxPerSource` | Cap per source (default 150). |
| `minRelevanceScore` / `maxAgeDays` | Quality/freshness filters. |
| `previousDatasetId` | Prior run's dataset → new/expired/deadline-change detection. |

#### Example inputs

```json
{ "countries": ["Germany","Netherlands"], "categories": ["Software Engineering","Data Science"] }
```

```json
{ "sources": ["greenhouse","lever"], "greenhouseCompanies": ["stripe","gitlab"], "leverCompanies": ["ramp"], "keywords": ["data"] }
```

```json
{ "countries": ["France"], "locationType": "remote", "previousDatasetId": "<PREV_DATASET_ID>" }
```

### Output

#### 1) Internships (default dataset — active, ranked by relevance)

```json
{
  "id": "3f9a1c22b7e04d18",
  "jobTitle": "Data Science Internship (m/f/d)",
  "company": "Example GmbH",
  "country": "Germany", "city": "Berlin",
  "locationType": "hybrid",
  "industry": "Data Science", "department": "Data",
  "internshipType": "internship", "duration": "6 months",
  "salary": null,
  "requiredSkills": ["python","sql","machine learning"],
  "educationRequirements": "Bachelor",
  "experienceRequirements": null,
  "languageRequirements": ["English","German"],
  "applicationDeadline": null,
  "postingDate": "2026-08-20T00:00:00Z",
  "sourceWebsite": "themuse.com", "source": "themuse",
  "sources": ["themuse","arbeitnow"],
  "sourceUrls": [{ "source":"themuse","url":"https://..." }],
  "url": "https://...", "companyUrl": null,
  "description": "…",
  "relevanceScore": 82,
  "relevanceReasons": ["country match","role match","keywords: python"],
  "classifications": ["new","high-match"],
  "status": "active",
  "collectedAt": "2026-08-30T18:00:00Z",
  "lastVerifiedAt": "2026-08-30T18:00:00Z"
}
```

#### 2) Changes (named dataset `changes`)

```json
{ "id":"3f9a1c22b7e04d18", "changeType":"new", "jobTitle":"…", "company":"…", "detectedAt":"…" }
```

`changeType` ∈ `new` · `expired` · `deadline-changed` · `updated`. Run summary is in key-value store `OUTPUT`.

### Relevance score & classifications

Base 40 (already a validated internship) + country/city/role/keyword/language/location-type matches + freshness + multi-source corroboration → 0–100. Classifications: **new** (≤7d), **deadline-soon** (≤14d), **remote**, **high-match** (≥75), **likely-expired** (deadline passed or >90d old). Likely-expired listings are kept out of the active dataset but still surface in `changes` for historical tracking.

### Scheduling & history

Schedule daily (or several times/day) and pass the previous run's dataset ID as `previousDatasetId`. Each run emits new/expired/deadline-changed/updated events, so you can maintain a live + historical view.

### Website / database

`db/schema.sql` is a ready PostgreSQL schema with proper indexes (country, city, company, industry, location\_type, deadline, posting\_date, relevance, GIN on skills/languages/classifications) plus `internship_sources`, `internship_changes`, and `saved_searches` (alerts). Load the default dataset into `internships` (upsert on `id`, pattern included in the SQL) and the `changes` dataset into `internship_changes`. The website then queries/filter/paginates against these tables. **The website + query API is a separate build** from this Actor.

### Reliability

Per-source fail-safe (one dead source never stops the run), retries with backoff, concurrency across sources, HTML→text normalization, and deterministic ids for stable dedup/upsert.

### Limitations

- Internship detection is keyword/heuristic (multi-language: EN/DE/FR/ES/IT/NL/PT/PL) — very accurate on explicit sources, best-effort on free-text boards.
- Country/city are parsed from location strings via a curated hint list (extend `COUNTRY_HINTS` in `normalize.js`).
- Deadlines exist mainly on Greenhouse; other sources rarely publish them.
- LinkedIn/Indeed are out of scope by design (ToS/anti-bot).

# Actor input Schema

## `countries` (type: `array`):

Filter to these countries, e.g. "Germany", "Netherlands", "France". Empty = all.

## `cities` (type: `array`):

Optional city filter, e.g. "Berlin", "Amsterdam".

## `categories` (type: `array`):

Job categories/roles, e.g. "Software Engineering", "Data Science", "Finance". Used both to query The Muse and to filter.

## `keywords` (type: `array`):

Free-text keywords a listing must match (title or description), e.g. "machine learning", "react".

## `locationType` (type: `string`):

Preferred work arrangement.

## `languages` (type: `array`):

Only keep internships requiring these languages (e.g. "English", "German"). Empty = any.

## `sources` (type: `array`):

Which public sources to crawl.

## `greenhouseCompanies` (type: `array`):

Company board tokens for Greenhouse ATS (e.g. "stripe", "gitlab"). Only used if 'greenhouse' is in Sources.

## `leverCompanies` (type: `array`):

Company slugs for Lever ATS. Only used if 'lever' is in Sources.

## `maxPerSource` (type: `integer`):

Cap on records fetched per source (before filtering).

## `minRelevanceScore` (type: `integer`):

Only output internships at or above this score (0–100). 0 = keep all.

## `maxAgeDays` (type: `integer`):

Drop listings older than this many days (freshness). 0 = no limit.

## `previousDatasetId` (type: `string`):

Dataset ID of a previous run — enables new / expired / changed-deadline detection across scheduled runs.

## `proxyConfiguration` (type: `object`):

Optional. These public APIs work fine without a proxy. Enable only if a run's IP gets rate-limited.

## Actor input object example

```json
{
  "countries": [
    "Germany",
    "France"
  ],
  "categories": [
    "Software Engineering",
    "Data Science"
  ],
  "locationType": "any",
  "sources": [
    "themuse",
    "arbeitnow",
    "remotive"
  ],
  "maxPerSource": 150,
  "minRelevanceScore": 0,
  "maxAgeDays": 60,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `internships` (type: `string`):

Active internships ranked by relevance. Change events (new/expired/deadline-changed/updated) are in the named dataset 'changes'; the run summary is in the key-value store record OUTPUT.

# 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 = {
    "countries": [
        "Germany",
        "Netherlands"
    ],
    "categories": [
        "Software Engineering",
        "Data Science"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("tuhin/global-internship-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 = {
    "countries": [
        "Germany",
        "Netherlands",
    ],
    "categories": [
        "Software Engineering",
        "Data Science",
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("tuhin/global-internship-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 '{
  "countries": [
    "Germany",
    "Netherlands"
  ],
  "categories": [
    "Software Engineering",
    "Data Science"
  ]
}' |
apify call tuhin/global-internship-finder --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,tuhin/global-internship-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/N5T3tgkVp58HMvxAy/builds/uMQJ9bCf53d0mWJ8g/openapi.json