Prepare for your NVIDIA deep learning engineer interview
GPU optimization and AI system design cases calibrated to NVIDIA’s performance-obsessed, hardware-software co-design culture.
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The Interview
What NVIDIA is looking for
Systems Design Interview
AI Systems & GPU Architecture
01Memory Bandwidth vs Compute Bottleneck Analysis
02CUDA Kernel Optimization Strategy
03Model Parallelism & Distributed Training
04Inference Throughput Optimization
ML Engineering Interview
Deep Learning Engineering
01Mixed Precision & Quantization
02Transformer Architecture Tradeoffs
03Profiling & Performance Analysis
04Hardware-Aware Model Design
Behavioral Interview
Behavioral Interview
01Technical Depth & Ownership
02Performance Engineering Mindset
03Cross-Team Collaboration
04Impact & Influence
GPU memory hierarchy and bandwidth optimization//Hardware-software co-design for LLM inference//CUDA kernel and model parallelism profiling
Practice Library
