OpenJobs

Scrape jobs from any careers page in 3 lines of code. No custom scrapers needed.

from openjobs import scrape_careers_page

jobs = scrape_careers_page("https://stripe.com/jobs")
print(f"Found {len(jobs)} jobs")  # Found 142 jobs

Works with JavaScript-heavy sites, React/Next.js SPAs, and complex ATS systems.

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Why OpenJobs?

Feature	OpenJobs	Scrapy	BeautifulSoup	Selenium
Works on any site	Yes	No (custom spider per site)	No (static HTML only)	Yes (but slow)
Handles JavaScript	Yes (Firecrawl)	No	No	Yes
AI extraction	Yes (Gemini)	No	No	No
Setup time	30 seconds	Hours	Hours	Minutes
Maintenance	Zero	High	High	Medium

The problem: Every careers page has different HTML. Scrapy/BeautifulSoup need custom code per site. Selenium is slow and breaks often.

The solution: OpenJobs uses Firecrawl (JS rendering) + Gemini AI (smart extraction) = works everywhere, no maintenance.

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Install

pip install openjobs

Quick Start

from openjobs import scrape_careers_page

# Scrape any careers page
jobs = scrape_careers_page("https://linear.app/careers")

for job in jobs:
    print(f"{job['title']} - {job['location']}")

Environment variables needed:

export GOOGLE_API_KEY=your_key  # Free: https://aistudio.google.com/apikey

That's it. No Firecrawl key needed for basic usage (uses cloud with generous free tier).

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Features

Find Careers Page URL

Don't know the exact URL? OpenJobs finds it:

from openjobs import discover_careers_url

url = discover_careers_url("stripe.com")
# Returns: https://stripe.com/jobs/search

AI Enrichment

Extract tech stacks, salary ranges, and categorize jobs:

from openjobs import scrape_careers_page, process_jobs

jobs = scrape_careers_page("https://figma.com/careers")
enriched = process_jobs(jobs, enrich=True)

for job in enriched:
    print(f"{job['title_original']}")
    print(f"  Category: {job['category']}")
    print(f"  Tech: {job.get('tech_stack', [])}")

Filter by Category

# Only engineering jobs
eng_jobs = process_jobs(jobs, enrich=True, filter_categories=["Software Engineering"])

Self-Hosted (Unlimited Free)

Run Firecrawl locally for unlimited scraping:

git clone https://github.com/federicodeponte/openjobs.git
cd openjobs && docker compose up -d

export FIRECRAWL_URL=http://localhost:3002

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Output

{
  "company": "Linear",
  "title": "Senior Software Engineer",
  "department": "Engineering",
  "location": "Remote (US/EU)",
  "job_url": "https://linear.app/careers/...",
  "slug": "linear-senior-software-engineer",
  "date_scraped": "2025-01-08T10:00:00"
}

With enrichment:

{
  "category": "Software Engineering",
  "subcategory": "Backend Engineer",
  "tech_stack": ["TypeScript", "PostgreSQL", "Redis"],
  "experience_years": "5+",
  "salary_range": "$150,000 - $200,000"
}

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Supported Sites

Works with most careers pages:

Type	Examples	Status
Company sites	stripe.com, linear.app, figma.com	Supported
JavaScript SPAs	React, Next.js, Vue apps	Supported
ATS platforms	Lever, Greenhouse, Ashby	Supported
Heavy SPAs	Retool, Airtable, Vercel, Notion	Supported
Job boards	LinkedIn, Indeed, Glassdoor	Blocked (ToS)

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API Reference

Function	Description
scrape_careers_page(url)	Scrape jobs from a careers page
discover_careers_url(domain)	Find careers URL from domain
process_jobs(jobs, enrich=True)	Enrich with AI categorization
scrape_with_firecrawl(url)	Get page content as markdown
extract_jobs_from_markdown(md)	Extract jobs from markdown

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Environment Variables

Variable	Required	Description
GOOGLE_API_KEY	Yes	Gemini API key (free)
FIRECRAWL_URL	No	Self-hosted Firecrawl URL
FIRECRAWL_API_KEY	No	Firecrawl cloud key (500 free/mo)

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How It Works

URL → Firecrawl (renders JS) → Gemini AI (extracts jobs) → Structured JSON

1. Firecrawl renders JavaScript and returns clean markdown
2. Fallback extracts embedded JSON from React/Next.js data
3. Gemini AI parses job listings intelligently
4. Output returns structured job data

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Contributing

git clone https://github.com/federicodeponte/openjobs.git
cd openjobs
pip install -e ".[dev]"
make test

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License

MIT