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Microsoft·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jun 2026Remote

Summary

Microsoft SWE interview with a deep-dive into LLM decoding logic. The coding portion was more ML-adjacent than I expected, less leetcode and more 'do you actually understand how these models work under the hood.'

Questions Asked (1)

Q1

Implement greedy decoding for a language model. At each step you receive a logit vector over the vocabulary, pick the argmax token, append it to the sequence, and feed it back as input. Stop at an end-of-sequence token or after N tokens. Also discuss numerical stability, tie-breaking, and how this compares to sampling-based approaches.

Algorithms & Data StructuresTechnical Trade-offsSystem Design
Author's notes

I knew greedy decoding conceptually but writing it out cleanly under pressure was a different story.

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AI HintsAI Generated

Suggested Approach

Start by outlining the greedy decoding loop: at each step, compute logits, select the argmax token, append it, and feed it back until EOS or max length. Then discuss numerical stability (e.g., using log-softmax or subtracting max logit), tie-breaking strategies (e.g., lowest index), and compare greedy to sampling methods like top-k, top-p, and temperature scaling, highlighting trade-offs in determinism, diversity, and quality.

Pro tip: Mention that in practice, you'd use a framework like Hugging Face's generate with do_sample=False, but implementing it manually shows deeper understanding; also note that greedy decoding can lead to repetitive or dull outputs, so it's often used for tasks requiring deterministic answers.

1. Outline the greedy decoding algorithm

Describe the iterative process: given input sequence, compute logits for next token, select argmax, append, and repeat until EOS or max length. Mention that the model is called autoregressively.

2. Address numerical stability

Explain that logits can be large, causing overflow in softmax; use log-softmax or subtract max logit before exponentiation. Note that argmax is invariant to monotonic transformations, so stability mainly matters if probabilities are needed.

3. Discuss tie-breaking

Argmax may have ties; common strategies include picking the lowest index or using a stable sort. Mention that ties are rare with floating-point but can occur with quantized models or identical logits.

4. Compare to sampling-based approaches

Contrast greedy (deterministic, often repetitive) with sampling methods like temperature scaling, top-k, and top-p (nucleus) sampling, which introduce randomness for diversity but may sacrifice coherence. Mention beam search as a middle ground.

5. Conclude with practical considerations

Summarize when greedy is appropriate (e.g., tasks needing deterministic outputs) and its limitations. Mention that in production, you'd use optimized libraries but understanding the internals helps debug and customize.

Key Points to Mention

  • Autoregressive generation loop: logits -> argmax -> append -> feed back
  • Numerical stability: log-softmax, max subtraction, overflow prevention
  • Tie-breaking: lowest index, stable argmax, handling identical logits
  • Sampling methods: temperature, top-k, top-p, beam search
  • Trade-offs: determinism vs diversity, repetition vs coherence
  • Stopping criteria: EOS token, max length, and handling padding

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