# Welcome to the Jungle Scraper: WTJ Tech Jobs FR/EU + Salaries (`dltik/welcome-to-the-jungle-scraper`) Actor
Scrape Welcome to the Jungle tech jobs. Filter by keywords, country, contract (CDI/CDD/stage/alternance), city, remote, experience. Get title, company, salary, tech stack, full description, candidate profile. France, EU, global. For recruiters, ATS providers, dev sourcing teams. $0.50/1K jobs.
- **URL**: https://apify.com/dltik/welcome-to-the-jungle-scraper.md
- **Developed by:** [dltik](https://apify.com/dltik) (community)
- **Categories:** Business, Lead generation, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
$0.50 / 1,000 job scrapeds
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
## Welcome to the Jungle Scraper — Jobs & Companies
Extract job offers from **Welcome to the Jungle** with full details: title, company, salary, tech stack, description, contract type, remote policy. **HTTP-only, 256MB, $0.50 per 1,000 jobs.**
Filter by keywords, country, contract type, cities, remote, experience level. No proxy needed.
---
### What can this scraper do?
- **Search jobs by keyword** — full-text search across all WTJ offers
- **Filter by contract type** — full-time, internship, apprenticeship, freelance, VIE
- **Filter by country** — France, Germany, UK, Spain, Italy, Belgium, Netherlands, etc.
- **Filter by city** — Paris, Lyon, Berlin, London, etc.
- **Filter by remote** — full remote, partial, no remote
- **Filter by experience** — junior, mid, senior, expert
- **Enrich with details** — full description, profile required, missions, tech stack
- **Multi-language** — French and English versions of WTJ
---
### Why this scraper?
| Feature | This actor | Manual scraping |
|---------|-----------|-----------------|
| Reverse-engineered Algolia API | Yes (HTTP-only) | Need Playwright |
| Tech stack auto-detection | Yes | Manual parsing |
| Detail enrichment | Yes (parallel) | Slow |
| Memory | **256MB** | 1-2GB |
| Speed | **1000 jobs in ~30s** | Minutes |
| No proxy needed | Yes | Browser blocks |
| Price per 1,000 jobs | **$0.50** | — |
---
### Quick start
#### Search internships in AI/Data in France
```json
{
"keywords": ["data scientist", "machine learning", "ai engineer"],
"contractTypes": ["internship"],
"country": "FR",
"enrichDetails": true,
"maxResults": 100
}
````
#### All full-time jobs in Paris
```json
{
"contractTypes": ["full_time"],
"country": "FR",
"cities": ["Paris"],
"maxResults": 200
}
```
#### Remote jobs in tech across Europe
```json
{
"keywords": ["developer", "engineer"],
"contractTypes": ["full_time"],
"country": "ALL",
"remote": "full_remote",
"maxResults": 500
}
```
#### All jobs at a specific tech keyword
```json
{
"keywords": ["react"],
"country": "FR",
"experienceLevels": ["senior", "expert"]
}
```
***
### Input parameters
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `keywords` | array | `[]` | Search keywords (e.g. `["data scientist"]`). Empty = all jobs |
| `contractTypes` | array | `[]` | `full_time`, `internship`, `apprenticeship`, `temporary`, `freelance`, `vie` |
| `country` | string | `FR` | ISO 2-letter code or `ALL` |
| `cities` | array | `[]` | City names (case-insensitive) |
| `remote` | enum | `any` | `any`, `full_remote`, `punctual`, `partial`, `no_remote` |
| `experienceLevels` | array | `[]` | `junior`, `intermediate`, `senior`, `expert` |
| `language` | enum | `fr` | `fr` or `en` (WTJ language version) |
| `enrichDetails` | boolean | `true` | Fetch full job description + profile |
| `maxResults` | integer | `100` | Max jobs to extract (1-1000) |
***
### Output (per job)
```json
{
"type": "job",
"id": "criteo-data-scientist-intern-12345",
"title": "Data Scientist Intern",
"url": "https://www.welcometothejungle.com/fr/companies/criteo/jobs/...",
"company": "Criteo",
"company_slug": "criteo",
"company_logo": "https://cdn.welcometothejungle.com/...",
"company_size": "1000-5000",
"company_sectors": ["AdTech", "SaaS"],
"contract_type": "internship",
"duration_min_months": 6,
"duration_max_months": 6,
"city": "Paris",
"country": "France",
"country_code": "FR",
"address": "32 Rue Blanche, 75009 Paris",
"latitude": 48.882,
"longitude": 2.331,
"remote": "partial",
"salary_min": 1500,
"salary_max": 2000,
"salary_currency": "EUR",
"salary_period": "month",
"experience_level": "junior",
"education_level": "Bac+5",
"languages": ["French", "English"],
"profession_category": "Tech",
"profession_subcategory": "Data Science",
"summary": "Join our team to build ML models...",
"missions_short": "Build models, analyze data, deploy in production...",
"description": "Full HTML-stripped job description text...",
"description_html": "Full HTML version with formatting
",
"profile_required": "Master's degree in CS, experience with Python...",
"profile_html": "HTML version of profile required
",
"tech_stack": ["Python", "TensorFlow", "AWS", "Spark"],
"published_at": "2026-04-15T08:30:00Z",
"start_date": "2026-09-01T00:00:00Z",
"details_enriched": true
}
```
***
### How much does it cost?
**$0.0005 per job** = **$0.50 per 1,000 jobs**.
| Scenario | Jobs | Cost | Time |
|----------|------|------|------|
| 1 keyword, 50 jobs | 50 | $0.025 | ~10s |
| 5 keywords, 100 jobs | 100 | $0.05 | ~30s |
| All FR full-time, 500 jobs | 500 | $0.25 | ~60s |
| Maximum batch | 1,000 | $0.50 | ~120s |
256MB memory + HTTP-only = compute cost is ~$0.001 per run.
***
### Use cases
- **Recruitment** — automate job sourcing for ATS or CRM
- **Salary research** — analyze pay ranges by role and country
- **Tech stack intelligence** — track which technologies are in demand
- **Competitor monitoring** — see what jobs your competitors are hiring for
- **Market trends** — count jobs by sector, location, contract type
- **Student internship search** — filter by AI, data, full stack, etc.
- **Investor research** — track hiring trends as a growth signal
***
### Technical details
- **HTTP-only** — uses WTJ's internal Algolia API + REST detail endpoint
- **No browser** — 256MB, lightning fast
- **Public credentials** — Algolia App ID and search-only API key are public (used by WTJ frontend)
- **Pagination** — Algolia exposes up to 1,000 hits per query
- **Parallel enrichment** — 10 concurrent threads fetch job details
- **Tech stack detection** — auto-extracts mentioned technologies from descriptions
***
### Connect with Make, Zapier & n8n
This actor integrates with any automation platform via the Apify API.
#### Make (Integromat)
1. Add an **Apify module** in your Make scenario
2. Select **Run Actor** and choose this actor
3. Configure the input (paste your JSON)
4. Add a **Get Dataset Items** module
5. Connect to your ATS, Notion, Google Sheets, Slack
#### Zapier
1. Use the **Apify integration** on Zapier
2. Set trigger: **Actor Run Finished**
3. Action: **Get Dataset Items**
4. Send to your CRM, email, or spreadsheet
#### Webhooks
```python
run = client.actor("dltik/welcome-to-the-jungle-scraper").call(
run_input={...},
webhooks=[{
"eventTypes": ["ACTOR.RUN.SUCCEEDED"],
"requestUrl": "https://your-webhook-url.com"
}]
)
```
***
### API integration
#### Python
```python
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("dltik/welcome-to-the-jungle-scraper").call(run_input={
"keywords": ["data scientist", "ml engineer"],
"contractTypes": ["full_time", "internship"],
"country": "FR",
"enrichDetails": True,
"maxResults": 100,
})
for job in client.dataset(run["defaultDatasetId"]).iterate_items():
print(f"{job['title']} @ {job['company']} ({job['city']}) - {job['url']}")
```
#### curl
```bash
curl -X POST "https://api.apify.com/v2/acts/dltik~welcome-to-the-jungle-scraper/runs" \
-H "Authorization: Bearer YOUR_APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"keywords": ["python developer"],
"country": "FR",
"maxResults": 50
}'
```
***
### FAQ
**Does this work for all WTJ countries?**
Yes. WTJ has jobs in France, UK, Germany, Spain, Italy, Belgium, Netherlands, and more. Pass the ISO country code (FR, GB, DE, ES, etc.) or `ALL`.
**Is the data accurate?**
The actor uses WTJ's official internal API (the same one their frontend uses). Data accuracy is identical to what you see on welcometothejungle.com.
**Are credentials going to expire?**
The Algolia search API key is public (search-only, embedded in the WTJ frontend). It's stable but if WTJ rotates it, the actor needs a quick update.
**Can I get historical jobs?**
WTJ Algolia exposes only currently-active jobs. For historical data, you need to scrape regularly and store the results yourself (Apify Schedules can do this automatically).
**How fresh is the data?**
Real-time. Jobs published on WTJ appear in the Algolia index within minutes.
***
### Other scrapers by dltik
| Actor | What it does | Price |
|-------|-------------|-------|
| [Google Maps Email Extractor](https://apify.com/dltik/google-maps-email-extractor) | Extract emails, phones, WhatsApp from Google Maps | $3/1K |
| [Facebook Ads Scraper](https://apify.com/dltik/facebook-ads-scraper) | Scrape Meta Ad Library — ad copy, creatives, CTA links | $1/1K |
| [TikTok Scraper](https://apify.com/dltik/tiktok-scraper) | Scrape profiles, videos, hashtags, search, trending | $1/1K |
| [TikTok Video Downloader](https://apify.com/dltik/tiktok-video-downloader) | Download TikTok videos without watermark | $5/1K |
| [Reddit Scraper](https://apify.com/dltik/reddit-scraper) | Scrape posts, comments, profiles with sentiment analysis | $2/1K |
| [Trustpilot Scraper](https://apify.com/dltik/trustpilot-scraper) | Scrape reviews, ratings, company profiles with sentiment | $0.50/1K |
# Actor input Schema
## `keywords` (type: `array`):
List of keywords to search jobs (e.g. 'data scientist', 'react developer'). Leave empty to scrape ALL jobs matching other filters.
## `contractTypes` (type: `array`):
Filter by contract types: full\_time, internship, apprenticeship, temporary, freelance, vie. Leave empty for all.
## `country` (type: `string`):
ISO 3166-1 alpha-2 country code (FR, US, GB, DE, ES, IT, BE, NL, etc.). Use 'ALL' for any country.
## `cities` (type: `array`):
Filter by city names (e.g. \['Paris', 'Lyon']). Case-insensitive match on office city.
## `remote` (type: `string`):
Filter by remote policy.
## `experienceLevels` (type: `array`):
Filter by experience: junior, intermediate, senior, expert.
## `language` (type: `string`):
Welcome to the Jungle language version. fr (default) for France/EU, en for international.
## `enrichDetails` (type: `boolean`):
Fetch the full description, profile required, and missions for each job via the detail API. Slower but richer output. Recommended for fewer than 200 jobs.
## `maxResults` (type: `integer`):
Maximum number of jobs to extract. WTJ Algolia exposes up to 1000 results per query.
## Actor input object example
```json
{
"keywords": [
"data scientist"
],
"contractTypes": [],
"country": "FR",
"cities": [],
"remote": "any",
"experienceLevels": [],
"language": "fr",
"enrichDetails": true,
"maxResults": 100
}
```
# Actor output Schema
## `results` (type: `string`):
Dataset of job offers with title, company, location, contract, salary, full description, tech stack, and more.
# 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": [
"data scientist"
],
"country": "FR"
};
// Run the Actor and wait for it to finish
const run = await client.actor("dltik/welcome-to-the-jungle-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": ["data scientist"],
"country": "FR",
}
# Run the Actor and wait for it to finish
run = client.actor("dltik/welcome-to-the-jungle-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": [
"data scientist"
],
"country": "FR"
}' |
apify call dltik/welcome-to-the-jungle-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=dltik/welcome-to-the-jungle-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Welcome to the Jungle Scraper: WTJ Tech Jobs FR/EU + Salaries",
"description": "Scrape Welcome to the Jungle tech jobs. Filter by keywords, country, contract (CDI/CDD/stage/alternance), city, remote, experience. Get title, company, salary, tech stack, full description, candidate profile. France, EU, global. For recruiters, ATS providers, dev sourcing teams. $0.50/1K jobs.",
"version": "1.0",
"x-build-id": "F91YHqyOcAgt94sjw"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/dltik~welcome-to-the-jungle-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-dltik-welcome-to-the-jungle-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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/dltik~welcome-to-the-jungle-scraper/runs": {
"post": {
"operationId": "runs-sync-dltik-welcome-to-the-jungle-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/dltik~welcome-to-the-jungle-scraper/run-sync": {
"post": {
"operationId": "run-sync-dltik-welcome-to-the-jungle-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",
"properties": {
"keywords": {
"title": "Search keywords",
"type": "array",
"description": "List of keywords to search jobs (e.g. 'data scientist', 'react developer'). Leave empty to scrape ALL jobs matching other filters.",
"items": {
"type": "string"
},
"default": []
},
"contractTypes": {
"title": "Contract types",
"type": "array",
"description": "Filter by contract types: full_time, internship, apprenticeship, temporary, freelance, vie. Leave empty for all.",
"items": {
"type": "string"
},
"default": []
},
"country": {
"title": "Country",
"type": "string",
"description": "ISO 3166-1 alpha-2 country code (FR, US, GB, DE, ES, IT, BE, NL, etc.). Use 'ALL' for any country.",
"default": "FR"
},
"cities": {
"title": "Cities (optional)",
"type": "array",
"description": "Filter by city names (e.g. ['Paris', 'Lyon']). Case-insensitive match on office city.",
"items": {
"type": "string"
},
"default": []
},
"remote": {
"title": "Remote policy",
"enum": [
"any",
"full_remote",
"punctual",
"partial",
"no_remote"
],
"type": "string",
"description": "Filter by remote policy.",
"default": "any"
},
"experienceLevels": {
"title": "Experience levels (optional)",
"type": "array",
"description": "Filter by experience: junior, intermediate, senior, expert.",
"items": {
"type": "string"
},
"default": []
},
"language": {
"title": "WTJ language",
"enum": [
"fr",
"en"
],
"type": "string",
"description": "Welcome to the Jungle language version. fr (default) for France/EU, en for international.",
"default": "fr"
},
"enrichDetails": {
"title": "Enrich with full job details",
"type": "boolean",
"description": "Fetch the full description, profile required, and missions for each job via the detail API. Slower but richer output. Recommended for fewer than 200 jobs.",
"default": true
},
"maxResults": {
"title": "Max results",
"minimum": 1,
"maximum": 1000,
"type": "integer",
"description": "Maximum number of jobs to extract. WTJ Algolia exposes up to 1000 results per query.",
"default": 100
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```