Bloomberg·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Bloomberg coding screen for a software engineer role. One question, pretty self-contained, but the implementation details had more nuance than I expected going in.

Questions Asked (1)

Given a list of token sequences as training data, build a bigram language model that can predict the most likely next token for any given query token. You need to implement both the training logic and the query interface.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The core idea clicked fast: count how often each token follows another, then just return the argmax.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and assumptions, then outline the training phase where you count bigram frequencies from the token sequences. Next, describe the query interface that uses the counts to predict the most likely next token, and discuss trade-offs such as handling unseen tokens and memory usage.

Pro tip: Mention smoothing techniques like add-one (Laplace) smoothing to handle unseen bigrams, and discuss how to efficiently store the model (e.g., nested dictionaries or sparse matrices) to balance memory and speed.

1. Clarify requirements and assumptions

Ask about the size of the dataset, whether sequences are sentences or arbitrary token lists, and if there are special tokens (e.g., start/end). Confirm that the model should predict the most frequent next token, not a probability distribution.

2. Design the training phase

Iterate through each token sequence, and for each adjacent pair (token_i, token_{i+1}), increment a count in a data structure (e.g., a dictionary mapping token_i to a dictionary of next tokens and their counts).

3. Implement the query interface

For a given query token, look up its next-token counts, and return the token with the highest count. If the token is unseen, decide on a fallback (e.g., return a special 'unknown' token or the most frequent token overall).

4. Discuss trade-offs and optimizations

Address memory vs. speed: using nested dictionaries is simple but may be memory-heavy; consider sparse representations or pruning low-frequency bigrams. Also discuss smoothing for unseen bigrams and handling of out-of-vocabulary tokens.

5. Test and validate

Walk through a small example to verify the model works, and consider edge cases like empty sequences, single-token sequences, and query tokens that never appear as a first token in any bigram.

Key Points to Mention

Bigram model: probability of next token depends only on the current token.
Training: count co-occurrences of adjacent tokens.
Query: return argmax of next-token counts for the given token.
Handling unseen tokens: smoothing (e.g., Laplace) or fallback strategies.
Data structures: nested dictionaries or sparse matrices for efficiency.
Trade-offs: memory vs. speed, and simplicity vs. scalability.

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