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.

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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

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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	—

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Quick start

Search internships in AI/Data in France

{
  "keywords": ["data scientist", "machine learning", "ai engineer"],
  "contractTypes": ["internship"],
  "country": "FR",
  "enrichDetails": true,
  "maxResults": 100
}

All full-time jobs in Paris

{
  "contractTypes": ["full_time"],
  "country": "FR",
  "cities": ["Paris"],
  "maxResults": 200
}

Remote jobs in tech across Europe

{
  "keywords": ["developer", "engineer"],
  "contractTypes": ["full_time"],
  "country": "ALL",
  "remote": "full_remote",
  "maxResults": 500
}

All jobs at a specific tech keyword

{
  "keywords": ["react"],
  "country": "FR",
  "experienceLevels": ["senior", "expert"]
}

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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)

{
  "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": "<p>Full HTML version with formatting</p>",
  "profile_required": "Master's degree in CS, experience with Python...",
  "profile_html": "<p>HTML version of profile required</p>",
  "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

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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

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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

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

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

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.

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