Affirm·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Affirm software engineer interview, coding round, one question the whole time: implement an LRU cache with O(1) get and put. Pretty standard if you've seen it before, brutal if you haven't.

Questions Asked (1)

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

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The classic combo of a hashmap and a doubly linked list.

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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 the logic for get and put, and discuss edge cases and potential optimizations.

Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases and avoid null checks, and discuss thread-safety considerations if the cache might be accessed concurrently.

1. Clarify Requirements

Ask about expected capacity, concurrency needs, and whether keys/values are generic. Confirm that O(1) average time is required for both operations.

2. Choose Data Structures

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

3. Define Operations

Detail get: if key exists, move node to front (most recent) and return value; else return -1. Detail put: if key exists, update value and move to front; else insert new node at front, add to map, and if capacity exceeded, remove tail node and delete from map.

4. Handle Edge Cases

Discuss capacity 0 or 1, updating existing keys, and eviction when full. Mention sentinel nodes to simplify list operations.

5. Analyze Complexity and Trade-offs

Confirm O(1) time for both operations and O(capacity) space. Discuss alternatives like using OrderedDict in Python or LinkedHashMap in Java, and trade-offs with concurrency.

Key Points to Mention

Hash map provides O(1) access to cache entries.
Doubly linked list maintains recency order with O(1) insertion and deletion.
Sentinel head and tail nodes simplify edge cases.
Eviction removes the least recently used node (tail) when capacity is exceeded.
Thread-safety can be addressed with locks or concurrent data structures.
Language-specific implementations like LinkedHashMap or OrderedDict can simplify the code.

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