Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Interviewed for a software engineering role at Anthropic and got hit with an LRU cache design question. Pretty classic but the O(1) constraint and the follow-up on concurrency made it more involved than I expected.

Questions Asked (1)

Design and implement an LRU cache class with get and put operations, both running in O(1) average time. The cache should evict the least recently used entry when it exceeds capacity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the hashmap plus doubly linked list approach going in, which helped.

Create a free account to read the full note

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 edge cases.

Pro tip: Mention thread-safety and concurrency considerations upfront, as Anthropic values production-ready thinking; also, briefly discuss alternative implementations like using OrderedDict in Python and their trade-offs.

1. Clarify Requirements

Ask about capacity constraints, thread-safety, and expected operations. Confirm that get and put must be O(1) and that eviction is based on least recent use.

2. Choose Data Structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains usage order for O(1) updates and evictions.

3. Design the Class

Outline the class with a capacity, a map from keys to nodes, and pointers to head (most recent) and tail (least recent) of the list. Define node structure with key, value, prev, next.

4. Implement Core Operations

Describe get: if key exists, move node to head and return value; else return -1. Describe put: if key exists, update value and move to head; else create node, add to head, and if over capacity, remove tail and delete from map.

5. Analyze and Extend

Discuss time and space complexity, edge cases (capacity 0 or 1), and potential extensions like thread-safety or TTL.

Key Points to Mention

Hash map for O(1) key lookup and doubly linked list for O(1) order maintenance.
Sentinel nodes (dummy head and tail) to simplify edge cases in list operations.
Eviction policy: remove least recently used node from tail and delete from map.
Time complexity: O(1) average for both get and put; space complexity O(capacity).
Thread-safety considerations: use locks or concurrent data structures if needed.
Alternative implementations: e.g., using OrderedDict in Python, and their trade-offs.

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