Meta·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Apr 2026Remote

Summary

Meta MLE interview with a classic systems question that felt deceptively straightforward until I was actually in it. The coding portion leaned heavily on data structure fundamentals, and I left feeling like I'd done okay but not great.

Questions Asked (1)

Design and implement an LRU cache supporting get and put operations, both in O(1) time. Evict the least recently used entry when capacity is exceeded.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the answer conceptually but fumbled the implementation a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the design, implement the core methods, and discuss trade-offs and potential optimizations.

Pro tip: Mention that the same LRU eviction pattern is used in production ML systems like embedding caches and feature stores, showing you understand real-world applications beyond the interview question.

1. Clarify Requirements

Ask about capacity constraints, concurrency needs, and whether the cache should be thread-safe. Confirm that both get and put must be O(1) and that eviction is based on least recent use.

2. Choose Data Structures

Propose a hash map for O(1) key lookup and a doubly linked list to maintain usage order. Explain that the hash map stores key -> node references, and the list allows O(1) removal and insertion.

3. Design Operations

Detail how get moves the accessed node to the front (most recently used) and returns its value. For put, if the key exists, update value and move to front; if new, add to front and evict the tail if over capacity.

4. Implement and Test

Write clean code for the LRU cache class, handling edge cases like capacity 0 or 1. Walk through a small example to verify correctness and O(1) time complexity.

5. Discuss Trade-offs and Extensions

Talk about time vs. space trade-offs, thread-safety considerations (e.g., using locks or concurrent data structures), and how to extend to LFU or TTL-based eviction.

Key Points to Mention

Hash map provides O(1) access to cache nodes.
Doubly linked list maintains recency order with O(1) insert/delete.
Eviction removes the tail (least recently used) when capacity is exceeded.
Get operation must update recency by moving node to front.
Put operation must handle both existing and new keys, updating recency and evicting if needed.
Thread-safety can be achieved with locks or by using concurrent data structures, but may impact performance.

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