Glean·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jul 2026

Summary

Interviewed for a software engineer role at Glean and got hit with a BPE tokenizer implementation question. It's the kind of problem that looks like a coding exercise on the surface but really tests whether you understand how modern NLP tooling actually works under the hood. Took me a while to wrap my head around the non-overlapping pair counting logic.

Questions Asked (1)

Implement a Byte Pair Encoding tokenizer class with train, encode, and decode methods. The train method should learn merge rules from a string by repeatedly merging the most frequent adjacent token pair above a given threshold, using lexicographic order to break ties. Encode applies those learned rules in order, and decode reconstructs the original string from token ids.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This one genuinely surprised me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the BPE algorithm details and edge cases, then outline the class design with train, encode, and decode methods. Explain the training loop that counts adjacent pairs, selects the most frequent pair (lexicographic tie-break), merges it if above threshold, and records the merge rule. Describe how encode applies rules sequentially and decode reverses merges to reconstruct the string.

Pro tip: Mention that you would use a trie or hashmap to efficiently apply merge rules during encoding, and discuss the trade-off between merge rule order and encoding speed. Also, proactively address how to handle unknown tokens or characters not seen during training.

1. Clarify requirements and edge cases

Ask about the expected input format, threshold behavior (inclusive/exclusive), handling of ties, and whether the tokenizer should support multiple training iterations. Confirm if encode should return token IDs or merged strings.

2. Design data structures and class interface

Define the Tokenizer class with attributes for merge rules (ordered list of pairs), vocabulary mapping (token to ID), and reverse mapping. Plan how to store the training state and apply rules efficiently.

3. Implement training loop

Iteratively count adjacent token pairs, find the most frequent pair (break ties lexicographically), and if its frequency exceeds the threshold, merge all occurrences and record the merge rule. Repeat until no pair exceeds threshold.

4. Implement encode and decode

For encode, start with the input string split into characters, then apply each merge rule in order to produce final tokens, and map tokens to IDs. For decode, map IDs back to tokens and reverse the merge rules in reverse order to reconstruct the string.

5. Analyze complexity and discuss optimizations

Discuss time and space complexity of training (e.g., O(n^2) naive) and encoding (O(n * m) where m is number of rules). Suggest optimizations like using a priority queue for pair counts or a trie for rule application.

Key Points to Mention

BPE algorithm: iterative merging of most frequent adjacent pair above threshold, with lexicographic tie-breaking.
Data structures: use a dictionary to count pairs, a list to store merge rules in order, and a mapping for token IDs.
Encoding: apply merge rules sequentially to the input string, ensuring correct order of operations.
Decoding: reverse the merge rules in reverse order to reconstruct the original string from token IDs.
Edge cases: empty string, threshold not met, ties in frequency, and handling of unseen characters during encoding.
Complexity: training is O(n^2) in naive implementation; encoding is O(n * m) where m is number of merge rules; discuss potential optimizations.

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