AI Interview Prep delivers in-depth insights into advanced NLP, CV, RL, LLMs, ML System Design. We highlight common traps and proven strategies to help engineers excel in technical interviews.
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LLM System Design Interview #59 - The Constant-Volume Trap
You’re in a Senior ML Infrastructure Engineer interview at NVIDIA and the interviewer asks:
“Your DDP run is healthy on 8 GPUs. You scale to 64 across 8 nodes and per-GPU throughput drops 40%. Why and what should you have calculated befor...
LLM System Design Interview #58 - The FP8 Quantization Tax
You’re in a Senior ML Systems Engineer interview at Meta and the interviewer asks:
“You moved your matmuls to FP8 on H100s expecting a 2× speedup. You got 25%. Is FP8 overhyped, or did you do it wrong?”
Don’t say: “FP8 halves the bytes, s...
LLM System Design Interview #57 - The Expressiveness Trap
You’re in a Staff ML Engineer interview at Anthropic and the interviewer asks:
“Your model is 80 layers deep and your infra team is furious. Why does almost every production LLM land near a 100:1 width-to-depth ratio?”
Don’t say: “Deeper...
LLM System Design Interview #56 - The SwiGLU Capacity Trap
You’re in a Senior LLM Research Engineer interview at Meta and the interviewer asks:
“You swapped your ReLU FFN for SwiGLU, loss dropped 2%, and you shipped it. Why should I reject that result?”
Don’t say: “SwiGLU is a better activation,...
LLM System Design Interview #55 - The LayerNorm Trap
You’re in a Senior LLM Training Engineer interview at Anthropic and the interviewer asks:
“Your profiler says LayerNorm is 0.17% of your FLOPs but 25% of your step time. Do you optimize it?”
Most candidates say: “No, 0.17% of compute isn...
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Authors
The writers behind this newsletter.
I share: - AI interview questions & answers - Practical LLM engineering tips - Industry news & breakthroughs My goal is to help engineers, builders, and curious learners stay sharp, grow fast, and become world-class in AI..
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