Anthropic·Software Engineer·Technical Phone Screen·Intermediate

IntermediateRejected

Apr 2026Remote

Summary

Interviewed for a software engineering role at Anthropic. The technical portion involved an LRU cache problem and I ran out of time partly because of CoderPad setup issues. Rejection came Friday after a Monday interview.

Questions Asked (1)

Debug and extend an LRU cache implementation.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Lost a chunk of time just figuring out the CoderPad file path situation, which was embarrassing.

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AI HintsAI Generated

Suggested Approach

First, clarify the requirements and expected operations (get, put) and constraints (capacity, time complexity). Then, systematically debug the existing implementation by testing edge cases and tracing through operations, and finally extend it with additional features like thread safety or TTL, discussing trade-offs.

Pro tip: Demonstrate strong debugging skills by writing unit tests for edge cases (e.g., capacity 1, repeated gets, eviction order) before modifying code. When extending, discuss trade-offs such as using a doubly linked list vs. OrderedDict, and consider concurrency implications.

1. Clarify Requirements and Constraints

Ask questions to understand the expected operations, capacity limits, and performance requirements. Confirm whether the cache should be thread-safe or support additional features like TTL.

2. Analyze and Debug Existing Code

Review the provided implementation for correctness, focusing on data structures (e.g., hash map + doubly linked list) and edge cases. Identify bugs by tracing through operations like get, put, and eviction.

3. Test with Edge Cases

Mentally or verbally run through edge cases: capacity 1, get on missing key, updating existing key, eviction order, and concurrent access if applicable. This reveals hidden bugs and ensures robustness.

4. Propose and Implement Extensions

Suggest extensions such as thread safety (locks, concurrent data structures), TTL, or persistence. Discuss trade-offs (e.g., lock contention, complexity) and implement a chosen extension with clean code.

5. Summarize and Discuss Trade-offs

Recap the debugging process and extensions, highlighting time/space complexity and design decisions. Mention alternative approaches (e.g., using OrderedDict in Python) and their pros/cons.

Key Points to Mention

LRU cache typically uses a hash map for O(1) access and a doubly linked list for O(1) eviction/update.
Edge cases: capacity 1, get/put on non-existent keys, updating existing keys, and eviction of least recently used item.
Thread safety considerations: using locks (e.g., mutex) or concurrent data structures, and potential performance impacts.
Extensions: TTL (time-to-live) for entries, persistence, or size-based eviction policies beyond LRU.
Trade-offs: simplicity vs. performance, memory overhead of linked list nodes, and alternative implementations (e.g., OrderedDict).
Testing: unit tests for correctness, stress tests for concurrency, and profiling for performance.

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