James Shen

James Shen

Innovative full-stack developer with 20+ years of experience. Currently building a high-frequency trading system on Solana. Expert in React, .NET, Python, and cloud platforms.

Jobseeker - AI-Powered Job Search Automation

Jobseeker: AI-Powered Job Search on Autopilot

An open-source intelligent job search automation system that eliminates the tedious manual process of reviewing hundreds of job postings. Jobseeker automatically scrapes major job platforms, uses Claude AI to analyze each position against your resume, and surfaces only the best matches with detailed scoring and reasoning.

The Problem

Job searching traditionally involves hours of manually scrolling through irrelevant listings, reading job descriptions, and trying to assess fit. This repetitive, time-consuming process often leads to missed opportunities and application fatigue.

The Solution

Jobseeker automates the entire job discovery and analysis pipeline:

  • Automated Scraping: Collects job postings from SEEK, LinkedIn, and Indeed
  • AI-Powered Analysis: Uses Claude AI to evaluate each job against your resume
  • Intelligent Scoring: Rates every position (0-100) with detailed reasoning
  • Smart Filtering: Surfaces only the most relevant opportunities

Cost Efficiency

The entire analysis process costs just $1-2 USD to process hundreds of job postings using Claude’s API. This minimal cost delivers enormous time savings compared to manual review, making AI-powered job search accessible to everyone.

System Architecture

Jobseeker follows a clean three-stage pipeline:

Scanner → Analyzer → Review

This architecture mirrors professional trading systems (Scanner → Planner → Executor), demonstrating the versatility of the pattern across different domains. The separation of concerns enables:

  • Independent scaling of each component
  • Easy maintenance and debugging
  • Clear data flow and processing stages
  • Modular testing and validation

Technical Implementation

Built with Go

  • Modern Go implementation for performance and concurrency
  • Efficient web scraping with rate limiting
  • Concurrent job analysis for speed
  • Clean, maintainable codebase

AI Integration

  • Claude AI API for intelligent job analysis
  • Natural language processing of job descriptions
  • Resume matching with semantic understanding
  • Detailed reasoning generation for each score

Multi-Platform Support

  • SEEK job board integration
  • LinkedIn job scraping
  • Indeed platform support
  • Extensible architecture for additional sources

Development Process

Built with Claude Code

This project represents an interesting case study in AI-assisted development. Approximately 99% of the codebase was generated by Claude Code through iterative guidance and collaboration.

Key Development Insights:

  • Requires developer-level prompting: clear requirements, feature breakdown, and structured feedback
  • Functions as pair programming with an AI coding partner
  • Demands active debugging and architectural decisions from the human developer
  • Achieves remarkable development velocity when properly directed

The process isn’t fully autonomous - it requires developer expertise in problem decomposition, requirement specification, and system design. However, the execution speed is exceptional when these fundamentals are in place.

Learning Go Through Real Projects

This project served as a practical vehicle for learning Go while building production-ready software. The experience is part of a broader effort to port a Solana trading system from TypeScript to Go/Rust for improved performance.

Cross-Domain Architecture Patterns:

The similarity between trading and job search architectures demonstrates transferable software design:

  • Trading System: Scanner → Planner → Executor
  • Jobseeker: Scanner → Analyzer → Review

Both systems benefit from:

  • Separation of data collection from analysis
  • Clear processing pipelines
  • Parallel processing capabilities
  • Scalable architecture patterns

Key Features

Automated Job Discovery

  • Continuous monitoring of multiple job platforms
  • Configurable search parameters
  • Deduplication of job listings
  • Historical tracking of analyzed positions

Intelligent Analysis

  • Resume-to-job description matching
  • Skills gap identification
  • Company culture fit assessment
  • Salary expectation alignment
  • Location and remote work preference matching

User Experience

  • Clear scoring system (0-100)
  • Detailed analysis for each job
  • Reasoning transparency
  • Configurable filtering thresholds
  • Export capabilities for further review

Practical Applications

For Job Seekers:

  • Drastically reduce time spent on job search
  • Never miss relevant opportunities
  • Understand why jobs match or don’t match
  • Focus effort on best-fit positions

For Recruiters:

  • Reverse the process to match candidates to positions
  • Scale candidate screening
  • Provide transparent evaluation reasoning

Technical Advantages

Performance:

  • Go’s concurrency model enables parallel job analysis
  • Fast web scraping with efficient resource usage
  • Low memory footprint for continuous operation

Maintainability:

  • Clear separation of concerns
  • Well-structured Go packages
  • Comprehensive error handling
  • Extensible for new job platforms

Cost Effectiveness:

  • Minimal API costs ($1-2 per hundreds of jobs)
  • No ongoing subscription fees
  • Open-source and self-hosted
  • Full data privacy and control

Resources

GitHub Repository

Future Enhancements

Potential extensions include:

  • Automated application submission
  • Interview preparation based on job requirements
  • Salary negotiation insights
  • Company research automation
  • Network connection recommendations
  • Application tracking and follow-up automation

Broader Implications

Jobseeker demonstrates several important concepts:

  1. AI as a Force Multiplier: Properly applied AI can automate tedious cognitive tasks
  2. Accessible AI: Claude API’s pricing makes sophisticated AI analysis available to individual users
  3. AI-Assisted Development: Modern AI coding assistants can dramatically accelerate development when properly directed
  4. Cross-Domain Architecture: Good software patterns transcend specific application domains

This project proves that with minimal cost and development time, individuals can build powerful automation tools that solve real problems. The combination of AI analysis, efficient scraping, and thoughtful architecture creates a system that delivers genuine value to job seekers while remaining fully open-source and self-hostable.