# AI Hardware Engineer Roadmap

> A hardware-first AI engineering roadmap for CUDA, embedded systems, Jetson, FPGA, ML compilers, RISC-V, accelerator architecture, and edge AI deployment.

## Canonical Site

- [AI Hardware Engineer Roadmap](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/)
- [GitHub Repository](https://github.com/ai-hpc/ai-hardware-engineer-roadmap)

## Core Entry Points

- [Roles and Market Analysis](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Roles%20and%20Market%20Analysis/)
- [Phase 1 - Digital Foundations](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Phase%201%20-%20Foundational%20Knowledge/)
- [Phase 2 - Embedded Systems](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Phase%202%20-%20Embedded%20Systems/)
- [Phase 3 - Artificial Intelligence](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Phase%203%20-%20Artificial%20Intelligence/)
- [Phase 4 - NVIDIA Jetson](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Phase%204%20-%20Track%20B%20-%20Nvidia%20Jetson/1.%20Nvidia%20Jetson%20Platform/)
- [Phase 4 - ML Compiler and Graph Optimization](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Phase%204%20-%20Track%20C%20-%20ML%20Compiler%20and%20Graph%20Optimization/)
- [Phase 5 - Advanced Topics and Specialization](https://ai-hpc.github.io/ai-hardware-engineer-roadmap/Phase%205%20-%20Advanced%20Topics%20and%20Specialization/)

## Topic Summary

This roadmap teaches AI hardware engineering across the full stack:

- digital design and computer architecture
- operating systems and parallel programming
- embedded systems and firmware
- CUDA, GPU programming, and profiling
- Jetson deployment and edge AI systems
- FPGA development and accelerator prototyping
- ML compiler and graph optimization flows
- RISC-V and AI accelerator architecture
- AI agents, runtime systems, and production AI applications
