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Early-stage Startup·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Technical screen for an ML engineer role, basically one coding question around auto-regressive generation in PyTorch. Not super hard but you need to actually know the material going in.

Questions Asked (1)

Implement auto-regressive inference logic in PyTorch, including at least a couple of common token sampling strategies.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew the greedy decoding part cold but fumbled a bit when they pushed me to add temperature scaling and top-k.

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AI HintsAI Generated

Suggested Approach

Start by outlining the auto-regressive inference loop: given a prompt, repeatedly forward the model, sample the next token from the logits, append it to the sequence, and stop when an end-of-sequence token is generated or max length is reached. Then implement at least two sampling strategies—greedy and temperature-based top-k/top-p—and discuss trade-offs like speed, diversity, and determinism.

Pro tip: Mention that you would use key-value caching to avoid recomputing past tokens, and that you'd handle batching and padding for efficient inference. This shows awareness of real-world deployment constraints beyond just the algorithm.

1. Set up the inference loop

Initialize the input sequence with the prompt and define stopping criteria (e.g., max length, EOS token). Loop until stopping condition is met, each time feeding the current sequence to the model to get logits for the next token.

2. Implement sampling strategies

For greedy sampling, take argmax of logits. For temperature sampling, divide logits by temperature and sample from softmax. For top-k, keep only top k logits before sampling. For top-p (nucleus), keep smallest set of tokens whose cumulative probability exceeds p.

3. Append and continue

Append the sampled token to the input sequence and repeat. Optionally, use key-value caching to avoid recomputing past hidden states, significantly speeding up generation.

4. Handle batching and padding

If generating for multiple sequences, use padding and attention masks to handle variable lengths. Ensure that sampling is done independently per sequence in the batch.

5. Discuss trade-offs

Compare strategies: greedy is deterministic but can be repetitive; temperature adds randomness; top-k and top-p balance diversity and coherence. Mention computational cost and suitability for different applications.

Key Points to Mention

Auto-regressive generation: feeding the model's own output back as input.
Greedy vs. stochastic sampling: argmax vs. sampling from a distribution.
Temperature scaling: controlling randomness by scaling logits before softmax.
Top-k and top-p (nucleus) sampling: truncating the distribution to avoid low-probability tokens.
Key-value caching: storing past keys/values to avoid redundant computation.
Handling EOS token and max length to terminate generation.

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