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Microsoft·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Microsoft ML engineer interview with a pretty gnarly coding problem around stop-sequence detection for LLM inference. One question, but it went deep fast and touched on edge cases I hadn't fully thought through before.

Questions Asked (1)

Q1

Implement stop-token detection for an LLM inference loop. You're given a streaming token generator and a list of stop sequences (each can be multiple tokens long). Terminate generation the moment any stop sequence is fully matched and return only the text before the match. Be prepared to discuss correctness when a stop sequence spans multiple tokens and how to handle overlapping or prefix-conflicting stop sequences.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I started with the naive approach, buffer the last N tokens and check for a match after each new token, where N is the length of the longest stop sequence.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: we need to detect stop sequences in a streaming token generator, where sequences can span multiple tokens. Propose an efficient algorithm using a trie or Aho-Corasick automaton to track partial matches, and discuss how to handle overlapping or prefix-conflicting sequences. Emphasize correctness, edge cases, and performance considerations.

Pro tip: Mention that you would maintain a buffer of recent tokens and use a finite state machine to track matches, ensuring O(n) time complexity. Also, discuss the trade-off between memory and latency when buffering tokens for detection.

1. Clarify requirements and constraints

Ask about the nature of stop sequences: are they strings or token sequences? Can they overlap? What should happen if multiple stop sequences match at the same position? Clarify the expected output format.

2. Choose a detection algorithm

Propose using a trie or Aho-Corasick automaton to efficiently match multiple stop sequences simultaneously. Explain how to update the automaton state as each token is generated.

3. Handle streaming and buffering

Describe how to maintain a buffer of tokens to detect sequences that span multiple tokens. Discuss when to emit tokens (only after ensuring they are not part of a potential stop sequence).

4. Address overlapping and prefix conflicts

Explain how the automaton handles overlapping sequences (e.g., 'ab' and 'abc') and prefix conflicts (e.g., 'a' and 'ab'). Ensure the algorithm correctly identifies the earliest complete match.

5. Discuss correctness and edge cases

Cover edge cases: stop sequence at the very beginning, multiple matches, partial matches at the end of generation, and performance implications. Verify that the returned text excludes the stop sequence.

Key Points to Mention

  • Use of a trie or Aho-Corasick automaton for efficient multi-pattern matching.
  • Maintaining a buffer of tokens to handle multi-token stop sequences.
  • Handling overlapping sequences by tracking the longest partial match.
  • Ensuring O(n) time complexity with respect to the number of generated tokens.
  • Correctly truncating the output to exclude the stop sequence.
  • Considering memory vs. latency trade-offs in buffering strategies.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.