Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Anthropic software engineer interview with a classic LRU cache problem. The constraints were strict about O(1) for both reads and writes, so you couldn't get away with anything naive. Pretty standard coding round but the edge cases they listed made it clear they'd push on correctness.

Questions Asked (1)

Implement a fixed-capacity LRU cache supporting GET and PUT operations, both in O(1) time. When the cache is full, evict the least recently used key before inserting a new one.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The core idea clicked fast for me, hashmap plus doubly linked list, but I fumbled the eviction logic when capacity was 1.

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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. Explain the design, walk through edge cases, and discuss trade-offs and possible optimizations.

Pro tip: Mention that you would use a doubly linked list with sentinel nodes to simplify edge cases, and discuss thread-safety if the cache is used in a concurrent environment.

1. Clarify Requirements

Ask about cache capacity, key/value types, expected operations, and whether thread-safety is required. Confirm that both GET and PUT must be O(1).

2. Propose Data Structures

Suggest using a hash map for O(1) key lookup and a doubly linked list to maintain usage order. Explain how the map stores references to list nodes.

3. Detail Operations

Describe GET: if key exists, move node to front and return value; else return -1. Describe PUT: if key exists, update value and move to front; else insert at front and evict least recently used if at capacity.

4. Handle Edge Cases

Discuss capacity 0 or 1, updating existing keys, and eviction when full. Mention sentinel nodes to avoid null checks.

5. Discuss Trade-offs and Extensions

Talk about time vs. space, thread-safety (e.g., using locks or concurrent data structures), and possible variations like LFU or TTL.

Key Points to Mention

Hash map provides O(1) access to cache entries.
Doubly linked list maintains recency order with O(1) insertions, deletions, and moves.
Sentinel head and tail nodes simplify edge cases.
Eviction policy: remove the node just before the tail (least recently used).
Thread-safety considerations: use locks or concurrent data structures if needed.
Trade-offs: memory overhead of pointers, potential for cache thrashing, and alternative eviction policies.

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