Anthropic·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Anthropic software engineer interview, coding round focused on a classic cache design problem. Pretty standard topic but they pushed hard on the implementation details and complexity guarantees, so you can't just wave your hands at it.

Questions Asked (1)

Design and implement an in-memory LRU cache with get and put operations, both running in O(1) time. Walk through your data structures, edge cases, and complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the answer going in, doubly linked list plus a hashmap, but explaining WHY you need both took more words than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (capacity, eviction policy, thread-safety) and then propose a hash map combined with a doubly linked list to achieve O(1) get and put. Walk through the implementation step-by-step, covering edge cases like updating existing keys, evicting the least recently used item when at capacity, and handling capacity 0. Conclude with complexity analysis and potential optimizations.

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

1. Clarify Requirements

Ask about capacity constraints, expected operations, thread-safety, and whether keys/values are generic. Confirm that both get and put must be O(1).

2. Propose Data Structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. The map stores key -> node, and the list orders nodes from most to least recently used.

3. Detail Operations

For get: if key exists, move node to front and return value; else return -1. For put: if key exists, update value and move to front; else create new node, add to front, and if capacity exceeded, remove tail node and delete its key from map.

4. Handle Edge Cases

Discuss capacity 0 (always evict), updating existing keys, and ensuring no memory leaks. Mention sentinel nodes to avoid null checks.

5. Analyze Complexity and Trade-offs

State that both operations are O(1) time and O(capacity) space. Discuss alternatives like using an ordered dictionary (e.g., Python's OrderedDict) or a combination of hash map and deque, and trade-offs with thread-safety.

Key Points to Mention

Hash map for O(1) lookup and doubly linked list for O(1) insertion/deletion and recency tracking.
Sentinel head and tail nodes to simplify boundary conditions.
Moving accessed nodes to the front (most recently used) on get and put.
Eviction of the least recently used node (tail) when capacity is exceeded.
Time complexity: O(1) for both get and put; space complexity: O(capacity).
Thread-safety considerations: using locks or concurrent data structures if needed.

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