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Anthropic·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Anthropic coding screen for a software engineer role. One problem, pretty focused on data structures and binary search. The problem itself was clean but the O(log n) constraint is where things get interesting.

Questions Asked (1)

Q1

Design a time-aware key-value store that supports setting a key with a timestamp and retrieving the value that was active at or before a given timestamp. GET queries must run in O(log n) time relative to the number of writes for that key.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The core idea clicked fast: store each key's history as a sorted list of (timestamp, value) pairs and binary search on GET.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: keys are strings, values can be any type, timestamps are monotonically increasing per key? Then propose a design where each key maps to a time-ordered data structure (e.g., balanced BST or sorted array) storing (timestamp, value) pairs. For GET, perform binary search to find the largest timestamp ≤ query timestamp, achieving O(log n) time.

Pro tip: Mention that if timestamps are monotonically increasing per key, you can append to a dynamic array and still binary search in O(log n), but if out-of-order writes are possible, a balanced BST or skip list is needed. Also discuss memory trade-offs and potential for versioning/compaction.

1. Clarify requirements and constraints

Ask about timestamp ordering, concurrency, persistence, and whether values can be deleted. Confirm that GET must be O(log n) in the number of writes for that key.

2. Choose per-key data structure

For each key, maintain a collection of (timestamp, value) pairs sorted by timestamp. Options: balanced BST (e.g., red-black tree), skip list, or sorted array if writes are append-only.

3. Implement SET operation

Insert the new (timestamp, value) into the per-key structure. If timestamps are unique per key, handle duplicates by overwriting or keeping latest. Ensure insertion maintains sorted order.

4. Implement GET operation

Binary search for the largest timestamp ≤ query timestamp. Return the associated value. If none exists, return null or a sentinel.

5. Analyze complexity and trade-offs

GET is O(log n) due to binary search. SET is O(log n) for BST/skip list, O(1) amortized for append-only array. Discuss memory overhead and potential optimizations like compaction.

Key Points to Mention

  • Per-key time-ordered data structure (e.g., balanced BST, skip list, or sorted array).
  • Binary search to find the largest timestamp ≤ query timestamp for O(log n) GET.
  • Handling of duplicate timestamps (overwrite or keep latest).
  • Complexity analysis: GET O(log n), SET O(log n) or O(1) depending on structure.
  • Trade-offs: memory vs. speed, concurrency control, persistence.
  • Edge cases: query timestamp before first write, after last write, empty key.

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