xAI·Machine Learning Engineer·Technical Phone Screen
May 2026
Deep technical screen for an ML Engineer role at xAI. The whole session was basically one long design problem around building a tokenizer from scratch, and it went pretty far into the weeds on Unicode, complexity analysis, and production trade-offs.
- Design a subword tokenizer for an LLM pretraining pipeline built on a prefix trie. Define the full API including build, tokenize, and detokenize methods.
- How would you handle Unicode in this tokenizer, specifically multi-byte UTF-8 sequences, emojis, and CJK characters?
- What normalization strategy would you apply before tokenization, and what are the trade-offs of choices like NFKC versus lowercasing?
- How should the tokenizer handle whitespace and punctuation, and what is your fallback strategy for unknown tokens?
- Analyze the time and space complexity of the trie-based tokenizer for both build and tokenize operations.
- How would you support incremental vocabulary updates without rebuilding the entire trie from scratch?
- What tests would you write to cover tricky tokenizer inputs?
- Compare the trie-based greedy tokenizer to BPE and WordPiece. What are the throughput and memory trade-offs in a production LLM pipeline?
“This was the core of the whole interview.” The rest of the author's notes on Machine Learning Engineer interview at xAI, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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