Capital One·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Capital One ML Engineer interview with a coding question focused on nucleus sampling. Pretty niche topic for a finance company but I guess they take their ML seriously. The implementation details were more involved than I expected going in.

Questions Asked (1)

Implement top-p (nucleus) sampling from a language model's logits, including softmax with optional temperature, sorting, cumulative probability thresholding, renormalization, and sampling. Also discuss numerical stability and how top-p compares to top-k and temperature sampling.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took me a second to get my footing.

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AI HintsAI Generated

Suggested Approach

Start by outlining the algorithm step-by-step: apply temperature scaling, compute softmax probabilities, sort in descending order, compute cumulative sum, find the smallest set whose cumulative probability exceeds p, zero out the rest, renormalize, and sample. Then discuss numerical stability techniques like subtracting the max logit before exponentiation and using log-space operations, and compare top-p to top-k and temperature sampling in terms of trade-offs.

Pro tip: Emphasize that top-p adapts the candidate set size based on the distribution's shape, which is crucial for maintaining diversity in high-confidence contexts and avoiding incoherence in low-confidence ones—this shows you understand practical deployment concerns.

1. Outline the algorithm

Describe the sequence: temperature scaling, softmax, sorting, cumulative probability, thresholding, renormalization, and sampling. Mention that temperature is optional but commonly used.

2. Implement with numerical stability

Explain how to avoid overflow/underflow by subtracting the max logit before exponentiation, and consider using log-softmax for stability. Show awareness of precision issues.

3. Handle edge cases

Discuss what happens when p is very small or very large, and how to ensure at least one token is kept. Mention that if no tokens exceed p, keep the top token.

4. Compare to top-k and temperature

Contrast top-p's dynamic candidate set with top-k's fixed size, and explain how temperature scales the distribution's sharpness. Highlight scenarios where each is preferred.

5. Discuss practical considerations

Mention computational complexity (sorting is O(n log n)), memory usage, and potential optimizations like using a heap for large vocabularies. Also note that top-p is often combined with temperature.

Key Points to Mention

Temperature scaling: logits divided by temperature before softmax; temperature >1 flattens, <1 sharpens.
Numerical stability: subtract max logit before exponentiation to prevent overflow; use log-sum-exp trick.
Sorting and cumulative sum: sort probabilities descending, compute cumulative sum, find cutoff where cumsum >= p.
Renormalization: after zeroing out tokens beyond cutoff, renormalize probabilities to sum to 1.
Top-p vs top-k: top-p adapts to distribution shape, top-k uses fixed number of tokens; top-p often yields more coherent and diverse outputs.
Temperature vs top-p: temperature affects all tokens' probabilities, top-p truncates the tail; they can be combined for fine-grained control.

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