Process, Questions & AI Prep Tips
AMD is the primary competitor to NVIDIA in GPUs and Intel in CPUs. Engineering interviews focus on GPU and CPU architecture, the ROCm open-source GPU software stack, HPC programming models, and AI accelerator design. AMD is making aggressive investments in the AI hardware market with its Instinct MI series GPUs, creating significant engineering opportunities for hardware architects and AI software engineers.
A 30-minute call about your background in GPU/CPU architecture, HPC, or AI software development, and your interest in AMD's specific product areas.
A 60-90 minute technical interview covering algorithms and domain-specific concepts relevant to your role — GPU architecture, parallel programming, or compiler design.
A design session covering GPU software stack architecture, AI accelerator design, or HPC cluster infrastructure depending on your target role.
Two to three rounds with domain experts covering deep technical design, coding, and behavioral interviews. AMD values practical engineering depth and collaborative problem-solving.
Explain the differences between AMD's RDNA (gaming) and CDNA (compute) GPU architectures.
How would you optimize a HIP/ROCm kernel for matrix multiplication on an MI300X GPU?
Design AMD's ROCm software stack architecture — how does it provide CUDA-like programming on AMD GPUs?
How would you build a distributed training system using AMD Instinct GPUs and ROCm?
Design a GPU memory allocator that handles HBM (High Bandwidth Memory) efficiently for AI workloads.
How would you implement a compute graph optimization pass for an AMD GPU compiler?
Design AMD's Infinity Fabric — the high-bandwidth chip-to-chip interconnect architecture.
How would you build a benchmarking and profiling tool for AMD GPU workloads?
Design a cloud GPU service that provisions AMD Instinct GPUs for AI training workloads.
Tell me about a time you worked on low-level performance optimization for compute hardware.
Study the ROCm ecosystem — AMD's open-source GPU software stack — including HIP (CUDA-compatible programming model), rocBLAS, MIOpen, and the compiler toolchain.
Understand how AMD's GPU architecture (CU structure, cache hierarchy, HBM memory) compares to NVIDIA's architecture to be able to discuss performance trade-offs.
Review AMD's CDNAarchitecture in detail for Instinct MI series GPU interviews — understanding compute versus graphics GPU design differences matters.
AMD competes on value and openness — be prepared to discuss why an open software stack (ROCm vs CUDA) matters strategically for AI customers.
Study the Infinity Fabric interconnect and how it enables AMD's chiplet-based CPU and GPU designs.
For AI software roles, understand how PyTorch, JAX, and other frameworks support AMD GPUs through ROCm backend and what gaps remain versus CUDA.
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