Software Engineer ML System Design Interview Questions
Ask a software engineer to design an ML system and the question quietly turns into an infrastructure question. These 144 reports come mostly from the labs and the hardware around them: Anthropic contributes 14, OpenAI 11, NVIDIA and Amazon 6 apiece, with Meta, Bytedance, Intuit and Google behind them. What gets probed is the machinery. A video generation service where the real problem is scheduling a GPU fleet. A 200-terabyte media migration feeding a training input pipeline. An enterprise research assistant whose retrieval has to produce citations someone can verify. Recommendation systems built from browsing history, where the interviewer wants the trade-offs stated rather than the maths derived. Throughput, storage layout, index and cache choices, cost per request and what degrades first under load carry more weight here than loss functions. Two things separate this slice from the same category asked of ML engineers: it skews early, with 64 questions in a technical screen against 51 onsite, and it skews difficult, with hard the largest band at 59 of 144. Every question has a written solution and three quarters of the page reads without premium.
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