Microsoft·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Microsoft ML Engineer interview, technical phone screen focused on NLP decoding algorithms. The main coding problem was a beam search implementation and the follow-up pushed into theoretical territory around length bias. Pretty deep for a phone screen.

Questions Asked (2)

Implement beam search decoding in Python. You're given a next-token probability simulator that takes the last token and returns a list of (token, probability) pairs. Maintain k candidate sequences, score them by cumulative log-probability, and stop when all beams hit end-of-sequence or a max length is reached. Return the final beams with their probabilities.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This took me longer to set up than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and the simulator interface, then outline the beam search algorithm step-by-step, emphasizing how to maintain k beams and score them using cumulative log-probabilities. Write clean, modular Python code with helper functions for initialization, expansion, and termination, and discuss trade-offs like beam width and length normalization.

Pro tip: Mention that you would use log-probabilities to avoid numerical underflow and consider length normalization to prevent bias toward shorter sequences; this shows practical experience with beam search in real systems.

1. Clarify requirements and interface

Confirm the simulator's behavior, the meaning of end-of-sequence token, and whether probabilities are already log-transformed. Ask about expected beam width k and max length.

2. Initialize beams and scores

Start with a single beam containing the start token (or empty sequence) with log-probability 0.0, and set up data structures to track sequences and their cumulative scores.

3. Iteratively expand and prune

At each step, for each active beam, get next-token probabilities from the simulator, compute new cumulative log-probabilities, collect all candidates, and select the top k sequences overall.

4. Handle termination conditions

Stop when all beams end with the end-of-sequence token or when the maximum length is reached. Ensure that completed beams are not expanded further.

5. Return final beams and probabilities

Convert log-probabilities back to probabilities (if needed) and return the top k sequences along with their scores, optionally applying length normalization.

Key Points to Mention

Use cumulative log-probabilities to avoid numerical underflow and simplify multiplication.
Maintain exactly k beams at each step, but allow completed beams to remain in the set if they are among the top k.
Consider length normalization (e.g., dividing by length^alpha) to prevent bias toward shorter sequences.
Efficiently manage data structures: use a priority queue or sort candidates to select top k.
Handle edge cases: empty input, k larger than vocabulary, and beams that never reach end-of-sequence.
Discuss trade-offs: larger k improves quality but increases computation; early stopping can save time.

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

What is the main drawback of vanilla beam search, and how would you fix it?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

Length penalty normalization.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining vanilla beam search and its main drawback: the lack of diversity in the beam, which can cause the search to miss high-probability sequences due to the beam search curse. Then propose a fix, such as diverse beam search or stochastic beam search, explaining how it addresses the issue and its trade-offs.

Pro tip: Mention that the drawback is not just about diversity but also about the beam search curse where increasing beam width can hurt performance; this shows deep understanding and connects to practical implications in large language models.

1. Define vanilla beam search

Briefly explain that vanilla beam search is a heuristic search algorithm that explores a fixed number of top candidates (beam width) at each step, commonly used in sequence generation tasks like machine translation.

2. Identify the main drawback

State that the main drawback is the lack of diversity among the beams, leading to suboptimal or repetitive outputs, and mention the beam search curse where increasing beam width can degrade performance.

3. Propose a fix

Suggest a solution such as diverse beam search, which adds a diversity penalty to encourage different beams, or stochastic beam search, which samples candidates instead of taking the top-k.

4. Explain how the fix works

Describe the mechanism: e.g., diverse beam search groups beams and penalizes similarity, while stochastic beam search introduces randomness to avoid local optima.

5. Discuss trade-offs and applications

Mention that these fixes can increase computational cost or complexity, and note when to use them, such as in open-ended generation tasks where diversity is crucial.

Key Points to Mention

Vanilla beam search keeps top-k hypotheses at each step based on cumulative log probability.
Main drawback: lack of diversity, leading to generic or repetitive outputs, and the beam search curse (performance can worsen with larger beam widths).
Diverse beam search: introduces a diversity penalty to encourage dissimilar beams.
Stochastic beam search: samples candidates from a distribution rather than taking top-k, reducing bias.
Trade-offs: increased computation, hyperparameter tuning, and potential decrease in BLEU scores for some tasks.
Applicability: especially important in tasks like dialogue generation, story generation, and machine translation where diversity matters.

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