Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Google SWE interview with a coding round focused on NLP-adjacent ML fundamentals. The bigram question sounds deceptively simple until you get to the follow-up about scale, which is where things got interesting.

Questions Asked (1)

Build a bigram language model from a training corpus. For each word, track which words follow it and how frequently. Then implement a predict(word) function that returns the next word using weighted random sampling based on those frequencies.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I got the basic structure down pretty fast, a dict of dicts mapping each word to its successors with counts.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then describe the data structures for storing bigram counts (e.g., nested hash maps). Explain the training process and the predict function using weighted random sampling, and discuss trade-offs like memory usage and smoothing.

Pro tip: Mention how you would handle unseen words or contexts (e.g., backoff to unigrams or use Laplace smoothing) to show robustness. Also, discuss how to scale the model for large corpora using distributed counting or approximate methods.

1. Clarify Requirements and Assumptions

Ask about corpus size, vocabulary, and whether to handle unseen words. Define the output format and whether the model should be case-sensitive or handle punctuation.

2. Design Data Structures

Propose a nested dictionary (or hash map) mapping each word to a dictionary of following words and their counts. Discuss memory implications and alternatives like sparse matrices.

3. Implement Training

Iterate through the corpus, tokenize, and update counts for each adjacent word pair. Consider preprocessing steps like lowercasing and handling out-of-vocabulary words.

4. Implement predict(word)

Retrieve the frequency map for the given word, compute total count, generate a random number, and select the next word via weighted sampling. Handle cases where the word is unseen.

5. Discuss Trade-offs and Extensions

Talk about time/space complexity, smoothing techniques, and how to extend to n-grams or neural models. Mention evaluation metrics like perplexity.

Key Points to Mention

Use of nested hash maps for efficient storage and lookup of bigram counts.
Weighted random sampling algorithm: cumulative distribution or random selection based on probabilities.
Handling unseen words or contexts: backoff to unigram, Laplace smoothing, or <UNK> token.
Time and space complexity: O(V^2) space in worst case, O(1) average lookup for prediction.
Scalability considerations: distributed counting (MapReduce) for large corpora, pruning rare bigrams.
Evaluation: perplexity, qualitative generation, and comparison to baseline unigram model.

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