Cohere·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

This was a stretch/bonus portion of a Cohere ML engineer interview, only unlocked after finishing the greedy and top-k decoding sections. The nucleus sampling implementation was the hardest part and felt like a real test of whether you actually understand the math behind sampling strategies.

Questions Asked (1)

Implement nucleus (top-p) sampling: given input tokens and a probability threshold p, at each decoding step sort tokens by probability, take the smallest set whose cumulative probability exceeds p, renormalize over that set, sample, and repeat autoregressively until EOS.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This only showed up after clearing greedy and top-k, so by the time I got here my brain was already pretty fried.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then outline the algorithm step-by-step, emphasizing the sorting, cumulative sum, thresholding, renormalization, and sampling. Discuss implementation details like numerical stability and efficiency, and mention trade-offs such as handling ties and the effect of p on diversity.

Pro tip: Mention that you can avoid full sorting by using a partial sort or a heap to find the top tokens until the cumulative probability exceeds p, which is more efficient for large vocabularies. Also, highlight the importance of numerical stability when renormalizing probabilities.

1. Clarify requirements and edge cases

Ask about input format (logits or probabilities), handling of ties, and whether p is a fixed threshold or can vary. Discuss edge cases like p=0, p=1, and when no tokens exceed p.

2. Outline the core algorithm

Describe the steps: sort tokens by probability descending, compute cumulative sum, select the smallest set with cumulative probability > p, renormalize, and sample from the filtered distribution.

3. Discuss implementation details

Explain how to implement efficiently: use a partial sort or heap to avoid full sorting, handle numerical stability by working in log-space or subtracting max logit, and ensure renormalization sums to 1.

4. Address autoregressive loop and stopping

Describe how to repeat the process for each decoding step, feeding the sampled token back as input, and stop when EOS is generated or max length is reached.

5. Analyze trade-offs and variations

Discuss the impact of p on diversity vs. coherence, compare to top-k sampling, and mention potential improvements like temperature scaling or dynamic p.

Key Points to Mention

Sorting tokens by probability and computing cumulative sum
Selecting the smallest set with cumulative probability > p
Renormalizing the probabilities over the selected set
Sampling from the renormalized distribution
Handling numerical stability (e.g., log-space, max subtraction)
Efficiency considerations (partial sort, heap) for large vocabularies
Autoregressive loop: feeding sampled token back and stopping at EOS
Trade-offs: diversity vs. coherence, comparison to top-k sampling

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