enigma·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Interviewed at Enigma for a software engineer role and got a system design question about extending a book search system to support autocomplete. Pretty focused technical round, not a lot of small talk.

Questions Asked (1)

You have an existing book title search system backed by an inverted index. Extend it to support autocomplete: given a partial word as input, return all titles where at least one word starts with that prefix.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight to a trie and felt pretty good about it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose extending the inverted index with a trie or prefix-optimized structure to map prefixes to word IDs, and finally link those to titles via the existing index. Discuss trade-offs between in-memory vs. distributed solutions, update strategies, and ranking of results.

Pro tip: Mention that you would return titles ranked by relevance (e.g., word frequency or title popularity) and handle multi-word prefixes by tokenizing the input, showing you think about user experience beyond basic functionality.

1. Clarify Requirements and Constraints

Ask about expected scale (number of titles, query throughput), latency requirements, update frequency, and whether results should be ranked. This ensures your design meets actual needs.

2. Design Data Structure for Prefix Matching

Propose a trie (prefix tree) or a sorted array with binary search to efficiently find all words starting with a given prefix. Discuss memory and performance trade-offs.

3. Integrate with Existing Inverted Index

Map each matching word to its postings list (title IDs) using the existing inverted index, then retrieve the corresponding titles. Consider storing word IDs in trie nodes for quick lookup.

4. Handle Updates and Scalability

Explain how to update the trie when titles are added/removed (e.g., batch updates, incremental insertion). For large scale, discuss sharding the trie or using a distributed cache like Redis.

5. Optimize and Rank Results

Describe ranking strategies (e.g., by word frequency, title popularity, or recency) and optimizations like caching frequent prefixes or limiting results to top N.

Key Points to Mention

Trie (prefix tree) for efficient prefix search, with trade-offs vs. other structures like ternary search trees or suffix arrays.
Integration with existing inverted index: using word IDs to fetch titles without scanning all titles.
Handling multi-word prefixes: tokenizing input and intersecting results from each word's prefix matches.
Scalability considerations: sharding, distributed cache, and read-heavy optimization.
Update strategy: incremental trie updates vs. periodic rebuilds, and consistency guarantees.
Ranking and limiting results: relevance scoring and pagination to avoid overwhelming the user.

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