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Xai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026Remote

Summary

Got a coding problem from xAI for a software engineer role that was basically implementing a radix tree (prefix-compressed trie) storing integer sequences. One meaty problem, no behavioral stuff, just pure data structure work.

Questions Asked (1)

Q1

Implement a RadixCache class that stores sequences of integers using a radix tree (prefix-compressed trie), supporting insert and a depth-first dump of the tree structure.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This one took me a minute to even parse correctly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline the radix tree node structure and insertion algorithm with prefix splitting. Explain the DFS dump format and complexity, and be prepared to discuss trade-offs like memory vs. speed and alternative data structures.

Pro tip: Mention that you would write unit tests for edge cases like inserting a prefix of an existing sequence or duplicate sequences, and discuss how the design would scale for large datasets.

1. Clarify requirements and constraints

Ask about expected input sizes, whether sequences can be empty, if duplicates are allowed, and the exact format for the DFS dump (e.g., node labels, indentation).

2. Design the radix tree node

Define a node with a label (substring of integers), a map from first integer to child, and an is_end flag. Explain how prefix compression works.

3. Implement insertion with prefix splitting

Walk down the tree matching integers; when a mismatch occurs, split the edge by creating a new intermediate node. Handle cases where the new sequence is a prefix of an existing path or vice versa.

4. Implement DFS dump

Perform a depth-first traversal, printing each node's label and depth. Use recursion or an explicit stack, and ensure the output is deterministic (e.g., sorted children).

5. Analyze complexity and trade-offs

State time complexity for insert and dump (O(L) per insert where L is sequence length, O(N) for dump where N is total nodes). Discuss memory overhead vs. a standard trie and alternatives like a hash set.

Key Points to Mention

  • Radix tree (prefix-compressed trie) reduces memory by merging single-child paths.
  • Insertion requires splitting edges when a partial match occurs, which is the trickiest part.
  • DFS dump should clearly show the tree structure, e.g., with indentation or parentheses.
  • Time complexity: O(L) per insert (L = sequence length) and O(N) for dump (N = number of nodes).
  • Space complexity: O(total unique integers stored) but with compression overhead.
  • Trade-offs: radix tree is efficient for prefix-heavy data but more complex than a hash set; consider if order or prefix queries are needed.

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