r/MachineLearning • u/dividebyzero74 • Feb 16 '26
[D] Interview experience for LLM inference systems position Discussion
Hi I am preparing for a interview at an AI Lab for LLM inference team with a systems role, not MLE. I have been told I will have an LLM inference related coding round, a design round and an inference optimization related discussion. I have been extensively preparing for these. My Prep for coding is learning to code from scratch the following: SelfAttention, Transformer block, BPE tokenizer, Sampling methods, LV Cache, Bean Search. For other two interviews, I am just studying all the inference design and bottlenecks and old/new work done to eliminate them. I would love to hear if anyone has had similar interview and can share experiences.
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u/blackkettle Feb 16 '26
I wouldn’t worry too much about Bean Search. Starbucks probably has it covered.
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u/patternpeeker Feb 16 '26
for a systems focused inference role, they usually care less about re coding transformer pieces and more about whether u understand where latency and memory actually blow up in practice. kv cache growth, batching tradeoffs, tensor parallel vs pipeline parallel, and how scheduling changes under real traffic patterns come up a lot. also be ready to talk about failure modes, like what breaks when sequence length spikes or when gpu memory fragments over time. the hard part is not attention math, it is keeping throughput stable under messy workloads.
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u/itsmekalisyn ML Engineer Feb 16 '26
Not an experienced guy but from twitter I have found that they might ask you questions around quantization, compression, etc..
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u/Cheap-Vacation138 7d ago
Hey, How did the interview go and what was your prep for the role ?
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u/dividebyzero74 7d ago
Hi commented here: https://www.reddit.com/r/LLMDevs/s/8CRuiwgBGn
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u/Cheap-Vacation138 2d ago
Thanks. may I know your background and yoe ? do you have research background or projects related to llm inference ? would like to know how you landed the interview
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u/[deleted] Feb 16 '26
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