ai-hpc/ai-hardware-engineer-roadmap
From Kernel-Level Parallel Programming to Custom AI Inference Accelerator Design — powered by NVIDIA GPUs, Jetson, and tinygrad
This is a free, self-paced curriculum for engineers who want to build the specialized hardware and software that power AI models like GPT or autonomous vehicles. It takes you from understanding digital logic and writing parallel code for GPUs to designing custom AI accelerators. The goal is to equip software, ML, embedded, and hardware engineers with the knowledge to create efficient AI systems from the chip up.
Use this if you are an engineer or computer science student aiming for roles in AI hardware, embedded AI, or optimizing AI infrastructure, and want a structured way to learn the full stack from chip design to AI applications.
Not ideal if you are looking for a high-level overview of AI applications without diving into low-level hardware, parallel programming, or chip architecture.
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Mar 19, 2026
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