Scale.ai·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026Remote

Summary

Scale.ai ML engineer coding round, done on Google Colab with some starter code already provided. Just one implementation task but it was trickier than it looked at first glance.

Questions Asked (1)

Using only NumPy, implement top-p (nucleus) sampling: apply softmax to a logit vector, sort by probability descending, find the smallest prefix whose cumulative probability meets or exceeds a threshold p, zero out everything outside that prefix, renormalize, and sample one token.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The starter code had softmax and test cases already written, which honestly helped a lot.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input shape and whether logits are 1D or batched, then walk through the algorithm step-by-step while writing clean NumPy code. Emphasize numerical stability and vectorization, and discuss trade-offs like sorting cost and handling edge cases.

Pro tip: Mention that you subtract the max logit before softmax to avoid overflow, and use np.cumsum on sorted probabilities to find the cutoff efficiently. Also note that top-p sampling is often used with temperature scaling, so you might apply temperature before softmax.

1. Clarify requirements and edge cases

Ask about input dimensions (1D vs. batched), dtype, and whether p is a scalar or per-example. Discuss handling of p=0, p=1, and ties in probabilities.

2. Compute softmax with numerical stability

Subtract the maximum logit before exponentiating, then normalize to get probabilities. This prevents overflow and underflow.

3. Sort and find nucleus

Sort probabilities in descending order, compute cumulative sum, and find the smallest set where cumsum >= p. Use np.searchsorted or boolean masking to identify the cutoff index.

4. Zero out and renormalize

Create a mask for the nucleus, set probabilities outside to zero, and renormalize the remaining probabilities to sum to 1.

5. Sample and return token

Use np.random.choice with the renormalized probabilities to sample an index, then map back to the original token ID if needed.

Key Points to Mention

Numerical stability: subtract max logit before softmax to prevent overflow.
Vectorization: use NumPy operations like np.sort, np.cumsum, and np.searchsorted to avoid Python loops.
Edge cases: handle p=0 (greedy), p=1 (full distribution), and ties in probabilities.
Efficiency: sorting is O(n log n); for large vocabularies, consider partial sorting or top-k pre-filtering.
Renormalization: after zeroing, divide by the sum of the nucleus probabilities to ensure a valid distribution.
Batching: if inputs are batched, ensure operations are applied per row using axis arguments.

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