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Microsoft·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Microsoft ML engineer interview with a meaty coding question on text generation decoding. One question, but it sprawled into a full design and implementation discussion that covered a lot of ground.

Questions Asked (1)

Q1

Implement beam search decoding and nucleus (top-p) sampling for an autoregressive language model, given a function that returns logits at each step. Discuss complexity, numerical stability, and the trade-offs between different decoding strategies.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

This was basically two coding problems stitched together with a design discussion at the end.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem setup: the model provides logits at each step, and we need to implement beam search and top-p sampling. Then, outline the algorithms step-by-step, emphasizing data structures (e.g., priority queues for beam search, sorting for top-p) and numerical stability techniques (e.g., log-softmax). Finally, discuss the trade-offs between decoding strategies in terms of quality, diversity, and computational complexity.

Pro tip: Mention that in practice, beam search can suffer from length bias and lack of diversity, so techniques like length normalization and diverse beam search are used. For top-p, note that it adapts to the model's confidence, unlike top-k which uses a fixed cutoff.

1. Clarify the problem and constraints

Confirm the interface: a function that returns logits for the next token given the current sequence. Discuss assumptions like batch size, sequence length, and whether we need to handle multiple sequences simultaneously.

2. Implement beam search

Describe the algorithm: maintain top-k hypotheses (beams), at each step compute log-probabilities for all possible next tokens, select top-k candidates across all beams, and update beams. Use a priority queue or sorting for efficiency. Mention length normalization to avoid favoring shorter sequences.

3. Implement nucleus (top-p) sampling

Explain the steps: compute softmax probabilities, sort tokens in descending order, compute cumulative sum, truncate the distribution when cumulative probability exceeds p, renormalize, and sample from the truncated distribution. Emphasize numerical stability by working in log-space and using log-sum-exp.

4. Analyze complexity and numerical stability

For beam search, complexity is O(k * V * T) where k is beam size, V vocabulary size, T sequence length. For top-p, sorting is O(V log V) per step. Discuss numerical stability: use log-softmax to avoid overflow/underflow, and for top-p, compute cumulative probabilities in log-space or use stable softmax.

5. Discuss trade-offs between decoding strategies

Compare beam search (high quality, low diversity, can be repetitive) vs. top-p sampling (more diverse, can be less coherent). Mention other strategies like greedy, top-k, temperature scaling, and when to use each. Highlight that top-p adapts to model confidence, while beam search is deterministic and can be computationally expensive.

Key Points to Mention

  • Beam search maintains multiple hypotheses and selects top candidates based on cumulative log-probabilities; length normalization is crucial to avoid bias towards shorter sequences.
  • Top-p sampling dynamically selects the smallest set of tokens whose cumulative probability exceeds p, then samples from that set; this adapts to the model's confidence.
  • Numerical stability: use log-softmax and log-sum-exp to avoid overflow/underflow; for top-p, compute cumulative probabilities in log-space or use stable sorting.
  • Complexity: beam search is O(k * V * T) time and O(k * V) space; top-p is O(V log V) per step due to sorting, but can be optimized with partial sorting.
  • Trade-offs: beam search produces high-quality but often repetitive and less diverse outputs; top-p sampling yields more diverse and creative outputs but may sacrifice coherence.
  • Practical considerations: beam search can be parallelized across beams; top-p requires sampling, which introduces randomness; both can be combined with temperature scaling.

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