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

Senior

Apr 2026

Summary

Scale.ai ML engineer interview focused on implementing token sampling from scratch, covering the full range of decoding strategies. Pretty implementation-heavy, no fluff.

Questions Asked (1)

Implement LLM token sampling from logits, supporting greedy (argmax), temperature scaling, top-k filtering, and top-p (nucleus) filtering. Return the sampled next-token id under the chosen strategy.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

More involved than it looks on the surface.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input format (logits shape, batch handling) and the expected output (single token id or per-sequence). Then outline a modular pipeline: apply temperature scaling, then top-k and/or top-p filtering, then sample or take argmax. Emphasize numerical stability and efficiency, and discuss trade-offs between strategies.

Pro tip: Mention that top-p filtering should be applied after sorting logits descending and computing cumulative probabilities, and that you must renormalize the filtered distribution before sampling. Also note that greedy is equivalent to temperature → 0 and can be handled as a special case.

1. Clarify requirements and edge cases

Ask about input shape (batch vs single), whether strategies can be combined (e.g., top-k then top-p), and how to handle ties or empty filtered sets. Confirm output format (token id per sequence).

2. Apply temperature scaling

Divide logits by temperature (if temperature > 0) to control randomness. For greedy, skip scaling and take argmax directly.

3. Implement top-k filtering

Keep only the k highest logits, set others to -inf, and renormalize. Use torch.topk or equivalent for efficiency.

4. Implement top-p (nucleus) filtering

Sort logits descending, compute softmax probabilities, find the smallest set whose cumulative probability ≥ p, zero out the rest, and renormalize.

5. Sample or select token

For greedy, return argmax. For stochastic, sample from the filtered distribution using torch.multinomial or equivalent. Return the token id.

Key Points to Mention

Numerical stability: subtract max logit before softmax to avoid overflow.
Order of operations: temperature → top-k → top-p (or as specified).
Renormalization after filtering to ensure probabilities sum to 1.
Efficiency: use vectorized operations and avoid unnecessary sorting when possible.
Handling edge cases: temperature=0 (greedy), k=0 or p=0, and ensuring at least one token remains.
Trade-offs: greedy is deterministic but may lack diversity; temperature increases randomness; top-k limits vocabulary; top-p adapts to distribution shape.

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