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Implement a trie-based tokenizer

xAIMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenML System Design

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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.

Implement a subword tokenizer for an LLM pretraining pipeline. Build it around a prefix trie to support greedy longest-prefix matching over a fixed vocabulary. Specify the API (e.g., build(vocab), tokenize(text) -> token IDs, detokenize(ids) -> text), Unicode handling (multi-byte UTF-8, emojis, CJK), normalization choices (e.g., NFKC, lowercasing), whitespace and punctuation rules, and unknown-token fallback. Analyze time and space complexity, describe how you would update the vocabulary incrementally, and propose tests for tricky inputs. Compare this trie-based approach to alternatives like BPE/WordPiece and discuss trade-offs for throughput and memory in production.