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Google Jobs Scraper - Pay-Per-Result

The most efficient, reliable, and developer-friendly Google Jobs scraper for Apify platform

Getting Started

1. Enter a job search keyword in the query field (required).
2. Optionally add location, country, and language to target results.
3. Set num_results to control how many listings to retrieve (10–1000).
4. Run the Actor; results appear in the Dataset tab, one row per job.
5. Export as JSON, CSV, or Excel from the dataset export menu.

This Actor uses price per results billing model. We also have a price per event billing model that operates the same way. If you prefer price-per-event billing, please see our Google Jobs Scraper - Price Per Event actor here.

Python Quick Start Example

A complete Python example using the Apify client is available on GitHub:

github.com/johnisanerd/Apify-Google-Jobs-Pay-Per-Result

Clone the repo, run uv sync, add your API key, and you are scraping Google Jobs in minutes.

🌟 Why Choose This Scraper?

The Google Jobs data scraper delivers enterprise-grade performance with these advanced capabilities:

Performance & Reliability: Built with modern Python async/await patterns and optimized for high-throughput scraping with intelligent rate limiting.

Cost-Effective: Provides for consistent, reliable results without the overhead of browser automation, cookies, or other rental costs.

Lightning-Fast Search & Retrieval: Search any keyword or job title across Google Jobs with blazing-fast performance. Retrieve comprehensive results in seconds, not minutes, with intelligent caching and optimization.

Precision Targeting & Advanced Filtering: Pinpoint exact locations with radius-based searches, filter by country and language, and apply sophisticated targeting parameters. Get precisely the job data you need, when you need it.

Rich, Structured Data Extraction: Extract complete job information, including application links, and comprehensive job descriptions. Our advanced parsing ensures you get clean, structured data ready for immediate use.

Enterprise-Grade Configuration & Flexibility: Built for developers and businesses who demand reliability. Highly configurable with intuitive controls, comprehensive error handling, and robust logging. Focus on your business logic while we handle the complexity of job scraping.

No Hidden Costs or Rental Fees: We do not charge monthly rentals, our scraper operates on a pay-per-result model. Scale up or down based on your actual needs without being locked into expensive subscriptions.

💰 Billing Model

This Actor uses a Pay-Per-Result (PPR) billing model. You are charged based on the number of job results produced and stored in the dataset.

Controlling Costs

To limit the maximum number of results and control costs, you can set the ACTOR_MAX_PAID_DATASET_ITEMS environment variable. For example:

• Setting ACTOR_MAX_PAID_DATASET_ITEMS=1000 will cap results at 1,000, resulting in a maximum charge of $15.00
• Setting ACTOR_MAX_PAID_DATASET_ITEMS=500 will cap results at 500, resulting in a maximum charge of $7.50

The Actor will automatically check your available funds before starting and will stop gracefully if insufficient funds are available.

What Counts as a Result?

Each job listing that is successfully pushed to the dataset counts as one result. This includes:

• Individual job postings with complete metadata
• Each job record with search parameters, timestamps, and job details

🚀 Features

Core Capabilities

• Advanced Search: Support for complex queries with location, country, and language filtering
• Intelligent Pagination: Automatic handling of Google Jobs pagination with configurable limits
• Location Targeting: Precise location-based searches with radius support
• Multi-Language: Support for international job markets

Data Quality

• Clean Output: Automatic structured data metadata for clean, production-ready data
• Structured Results: Consistent JSON structure across all job listings
• Comprehensive Fields: Job title, company, location, description, job highlights, related links, and posting metadata
• Metadata Tracking: Page-level analytics and search performance metrics

📖 Usage Examples

Basic Job Search Example

Search for a programmer job in San Francisco, CA.

{
"query":"Software Engineer",
"location":"San Francisco, CA"
}

Advanced Job Search Example

Search for a job in San Francisco, CA, with a radius of 25 miles, and return 1,000 results. Limit to 10 pages.

{
"query":"Software Engineer",
"location":"San Francisco, CA",
"country":"us",
"language":"en",
"google_domain":"google.com",
"num_results":1000,
"max_pagination":10,
"include_lrad":false,
"lrad_value":"25"
}

Company Filter Example

Filter out jobs from specific companies using case-sensitive matching. This example searches for "Barista" jobs and filters out all results from Starbucks.

Using text input (single or comma-separated):

{
"company_filter":"Starbucks",
"query":"Barista"
}

Using array input (recommended for multiple companies):

{
"company_filter_list":["Starbucks","Target","Walmart"],
"query":"Barista"
}

Note: The company filter uses case-sensitive matching, so "Starbucks" will match exactly. You can use either:

• company_filter: Single company name or comma-separated list (e.g., "Starbucks, Dunkin', Peet's Coffee")
• company_filter_list: Array of company names (takes precedence if both are provided)

Job Source Filter Example

Filter out jobs from specific job sources/platforms. This example filters out jobs from Indeed and ZipRecruiter.

Using text input:

{
"via_filter":"Indeed, ZipRecruiter",
"query":"Software Engineer"
}

Using array input (recommended):

{
"via_filter_list":["Indeed","ZipRecruiter","JobTarget"],
"query":"Software Engineer"
}

Note: Common job sources include "Indeed", "ZipRecruiter", "JobTarget", "Snagajob", "LinkedIn", etc. The via_filter_list array takes precedence over via_filter text input if both are provided.

🔍 Input References

Input Parameters

📊 Output Format

Job Listing Structure

Each row in the dataset represents one job listing. The envelope fields (total_jobs_found, pages_processed, search_metadata, pagination_info) are also stored per row for reference.

{
"title":"Senior Software Engineer",
"company_name":"Tech Corp",
"location":"San Francisco, CA",
"via":"via LinkedIn",
"description":"We are looking for a senior engineer to join our platform team...",
"job_highlights":[
{
"title":"Qualifications",
"items":["5+ years Python experience","Experience with cloud platforms"]
},
{
"title":"Benefits",
"items":["Health insurance","Remote work options"]
}
],
"related_links":[
{"link":"https://company.com/careers/123","text":"Apply on company site"},
{"link":"https://linkedin.com/jobs/view/123","text":"View on LinkedIn"}
],
"thumbnail":"https://encrypted-tbn0.gstatic.com/images?q=...",
"job_id":"eyJqb2JfdGl0bGUiOiJTZW5pb3IgU29mdHdhcmUgRW5naW5lZXIi...",
"detected_extensions":{
"posted_at":"2 days ago",
"schedule_type":"Full-time",
"experience_level":"Mid-senior level"
},
"extensions":["2 days ago","Full-time","Mid-senior level","Health insurance"],
"total_jobs_found":150,
"pages_processed":15,
"search_metadata":{
"query":"Software Engineer",
"location":"San Francisco, CA",
"country":"us",
"language":"en"
}
}

Note: Fields like salary, experience_level, and schedule_type are only present when Google surfaces that data for a listing. detected_extensions may be partial or empty for some jobs.

MCP Integration

This Actor is compatible with the Model Context Protocol (MCP). AI assistants and agents can call it directly as a tool without manual setup.

Use the Apify MCP server and pass query plus optional parameters as tool arguments. The Actor returns structured job listings the agent can process immediately.

Example agent prompt

"Search Google Jobs for 'Data Engineer' roles in Austin, TX, and return 50 results."

The agent calls this Actor automatically and receives structured JSON job listings in response.

FAQ and Limitations

Q: Why are some jobs missing salary or experience level data? Google does not surface this information for all listings. The detected_extensions object will be partial or empty when Google has not extracted it.

Q: What is the maximum number of results? num_results accepts 10–1,000. For larger datasets, run multiple searches with different queries or locations.

Q: What does the via field mean? It indicates the job board where Google found the listing (e.g., via LinkedIn, via Indeed, via ZipRecruiter).

Q: Does this Actor work for all countries? Yes. Use the country and google_domain parameters to target international job markets. Results depend on what Google Jobs indexes for that region.

Made with ❤️

Transform your job search automation with the most reliable and efficient Google Jobs scraper on the market.

Featured Tasks

Ready-to-run examples that show this API solving a specific problem. Each opens its own setup so you can run it on your account in one click.

• Backfill a job board with Google Jobs results - Backfill your job board with fresh Google Jobs listings.
• Export Google Jobs results to JSON by API - Pull structured Google Jobs results by API for any role.
• Find data scientist jobs in New York from Google Jobs - Find data scientist jobs in New York aggregated from Google Jobs.
• Find remote nursing jobs from Google Jobs by API - Find remote registered nurse jobs aggregated across job boards via Google Jobs.
• Find software engineer jobs in Austin from Google Jobs - Find software engineer jobs in Austin aggregated from Google Jobs.
• Export Google Jobs Listings to CSV

Last Updated: 2026.06.19