← Xai Interview Insights

Xai·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Interviewed for a software engineering role at xAI and got hit with an LRU cache design question. Pretty classic but the O(1) constraint is where things get interesting and where I probably could've been sharper.

Questions Asked (1)

Q1

Design and implement an LRU cache data structure with get and put operations, both running in O(1) average time.

Algorithms & Data StructuresSystem Design
Author's notes

I knew the answer involved a hashmap plus a doubly linked list but explaining WHY took me longer than it should have.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (capacity, eviction policy, thread-safety) and then describe the standard hash map + doubly linked list solution. Explain how each operation achieves O(1) average time and then implement the code, handling edge cases like updating existing keys and evicting the least recently used item.

Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases, and discuss how to make the cache thread-safe (e.g., with a mutex or concurrent data structures) if needed.

1. Clarify Requirements

Ask about capacity, eviction policy (LRU), and whether thread-safety is required. Confirm that get and put must be O(1) average time.

2. Choose Data Structures

Use a hash map for O(1) key lookup and a doubly linked list to maintain recency order. The map stores key -> node, and the list stores nodes with key-value pairs.

3. Define Operations

For get: if key exists, move node to front (most recently used) and return value; else return -1. For put: if key exists, update value and move to front; else insert new node at front and evict least recently used (tail) if capacity exceeded.

4. Implement with Edge Cases

Use sentinel head and tail nodes to avoid null checks. Handle capacity 0 or 1, updating existing keys, and eviction correctly.

5. Analyze Complexity and Discuss Optimizations

Explain that both operations are O(1) average due to hash map and linked list. Mention potential thread-safety using locks or concurrent structures, and trade-offs.

Key Points to Mention

  • Hash map provides O(1) average key lookup.
  • Doubly linked list maintains recency order with O(1) node removal and insertion.
  • Sentinel head and tail nodes simplify edge cases.
  • Eviction removes the least recently used item (tail of list).
  • Thread-safety can be achieved with a mutex or by using concurrent data structures.
  • Time complexity: O(1) average for both get and put; space complexity: O(capacity).

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