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ML System Design Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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Showing 331–332 of 332 results
- xAIMediumSoftware EngineerDesign a Retrieval-Augmented Generation (RAG) System
This question evaluates understanding and engineering competency for retrieval-augmented generation (RAG) systems, covering offline indexing and online query planes, retrieval quality, prompt assembly, grounded generation with citations, and production monitoring; the domain is ML System Design.
ML System DesignOnsite - xAIHardMachine Learning EngineerImplement a trie-based tokenizer
This question evaluates a candidate's competency in designing and implementing a production-grade subword tokenizer, covering trie-based longest-prefix matching, Unicode-aware text processing, normalization and casing impacts, whitespace/punctuation rules, fallback strategies, performance and memory trade-offs, versioning, and testing for edge cases. It is commonly asked in ML System Design interviews to assess practical implementation skills and conceptual trade-off reasoning for deterministic, scalable LLM preprocessing, and tests both practical application and conceptual understanding within the ML System Design domain.
ML System DesignTechnical Screen
xAI