AIJobs: AI Infrastructure and High-Performance Computing Job Aggregation Platform

Basic Info

• Author/Maintainer: ququyixiaozei
• Source: GitHub
• Original Link: https://github.com/ququyixiaozei/aijobs
• Release Time: 2026-05-30

AIJobs is an open-source daily crawler project focused on aggregating jobs in AI infrastructure, GPU/CUDA, machine learning systems, and inference performance engineering. It solves the information gap for specialized AI infra roles by automating data collection from multiple platforms, and provides RSS and JSON data sources for warpjobs.com.

With the rapid development of large model technology, the AI infrastructure (AI Infra) field faces unprecedented talent demand. Specialized roles (GPU cluster management, CUDA kernel development, distributed training frameworks, etc.) are scattered across various companies' recruitment systems, making it hard for job seekers to get comprehensive info.

Traditional recruitment platforms lack deep focus on this niche. Many top AI labs and infra companies use Greenhouse, Lever, Ashby, etc., which are dispersed, creating challenges for job seekers. AIJobs was built to bridge this gap via automated aggregation.

Multi-source Data Crawling Supports grabbing from mainstream recruitment systems: Greenhouse (widely used by tech firms/AI labs), Lever (startups/growth enterprises), Ashby (modern recruitment OS).

Precise Content Screening Focuses on:

• GPU & CUDA development (kernel optimization, CUDA programming, heterogeneous computing)
• Machine Learning Systems (ML platform engineering, training infra, model service)
• Inference & Performance Engineering (model inference optimization, latency/throughput improvement)
• AI Lab Infrastructure (large-scale cluster management, experiment platform development)

Data Output Formats

• RSS Feed: For users to subscribe via readers
• JSON API: For developers to integrate into custom apps or data analysis

For Job Seekers

• Info Aggregation: One-stop access to scattered jobs
• Domain Focus: Avoid irrelevant positions
• Timely Updates: Daily new job alerts

For Employers

• Wider Exposure: Reach more audiences via warpjobs.com
• Precise Targeting: Access professionals interested in AI infra

For Industry Observers

• Trend Insights: Analyze job frequency and skill requirements to understand tech trends
• Talent Demand Map: Reflect distribution of talent needs across companies/regions

Key engineering practices:

• Robust Error Handling: Deal with varying response formats and API changes
• Data Deduplication & Status Updates: Avoid duplicate jobs and track status changes (e.g., closed)
• Incremental Update Strategy: Balance data freshness and resource consumption
• Structured Data Extraction: Handle inconsistent formats to get standardized job info

As an open-source project:

• Transparency: Users can review crawling logic, screening criteria, and data sources
• Customizability: Developers can fork the project to modify rules or add new data sources
• Collaborative Improvement: Community can contribute to adding platforms or optimizing data extraction

AIJobs and warpjobs.com form a layered architecture:

• Data Layer (AIJobs): Open-source crawler and data processing logic, ensuring transparency and community participation
• Application Layer (warpjobs.com): Provides user-friendly interface with features like search, filtering, and subscription management

This separation allows the tech community to improve the data layer while end users enjoy a professional experience.

AIJobs is a small but precise open-source tool addressing the info needs of AI infrastructure talent market. In the large model era, AI infra engineers are increasingly important, making such specialized aggregation tools valuable.

For job seekers in AI infra or industry observers, AIJobs is a useful entry point. As AI industry grows, similar specialized info aggregation tools may become key infrastructure connecting talent and opportunities.