Microsoft·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Microsoft ML engineer interview, got a sequence generation coding problem that was essentially a stripped-down language model decoding exercise. Pretty focused on implementation details and interface design rather than theory.

Questions Asked (1)

Implement greedy decoding for a sequence generation model. At each step, select the token with the highest probability from the model's output distribution and feed it back as input, continuing until an end-of-sequence token is produced or a max length is reached. Define a clear interface for the model's next-token function and return the full generated sequence.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

The core of it is just argmax at each step, but I spent too long overthinking whether they wanted me to handle log-probabilities vs raw logits.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the interface for the model's next-token function, specifying inputs (e.g., current sequence) and outputs (probability distribution over vocabulary). Then outline the greedy decoding loop: at each step, select the token with the highest probability, append it to the sequence, and stop when EOS is generated or max length is reached. Finally, discuss trade-offs and potential optimizations.

Pro tip: Mention that greedy decoding is deterministic and fast but can lead to suboptimal sequences; briefly contrast with beam search to show awareness of alternatives. Also, emphasize handling edge cases like EOS as the first token or max length reached.

1. Define the model interface

Specify the next-token function signature, e.g., `next_token_probs(sequence: List[int]) -> List[float]`, which returns a probability distribution over the vocabulary given the current sequence.

2. Initialize the sequence

Start with a beginning-of-sequence (BOS) token or an empty list, depending on the model's convention. Set up a loop with a maximum length constraint.

3. Implement the decoding loop

At each step, call the next-token function with the current sequence, select the token with the highest probability (argmax), and append it to the sequence.

4. Define stopping conditions

Stop if the selected token is the end-of-sequence (EOS) token or if the sequence length reaches the maximum allowed length. Return the generated sequence (excluding or including EOS as per requirement).

5. Discuss trade-offs and optimizations

Mention that greedy decoding is efficient but may not yield globally optimal sequences. Optionally, discuss caching, batching, or using log-probabilities for numerical stability.

Key Points to Mention

Interface design: clear input/output types, handling of variable-length sequences, and batch support if needed.
Argmax selection: using argmax on probabilities or logits, and ensuring deterministic tie-breaking.
Stopping criteria: EOS token and max length, with proper handling of edge cases (e.g., EOS at first step).
Efficiency considerations: time complexity O(max_length * cost of model call), potential for caching model states.
Trade-offs: greedy vs. beam search, determinism, and quality of generated sequences.
Numerical stability: working with log-probabilities to avoid underflow, and converting back if needed.

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