Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Interviewed for a software engineering role at Anthropic and got a classic systems design-meets-coding hybrid question. Nothing too exotic, but the follow-up discussion on trade-offs is where it got interesting.

Questions Asked (1)

Implement the core operations of a task management system: adding a task, fetching a task by ID, and updating its fields. Then walk through your choice of in-memory data structure and the trade-offs involved.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with a HashMap keyed on task ID, which felt obvious, and they let me run with it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., concurrency, persistence, scale) and defining the Task data model. Then implement the three operations using an in-memory hash map for O(1) access, and discuss trade-offs like memory overhead, ordering, and thread safety.

Pro tip: Mention that you would use a concurrent hash map (e.g., ConcurrentHashMap in Java) to handle concurrent access, and discuss how you might add indexing for secondary lookups (e.g., by status or assignee) if needed.

1. Clarify Requirements and Define Data Model

Ask about expected scale, concurrency needs, and whether tasks need ordering or querying by other fields. Define a Task class with fields like id, title, description, status, priority, assignee, and timestamps.

2. Choose In-Memory Data Structure

Select a hash map (e.g., HashMap or ConcurrentHashMap) for O(1) average-case add, get, and update by ID. Justify the choice based on the operations and discuss alternatives like trees or lists.

3. Implement Core Operations

Write pseudocode or actual code for addTask (put with unique ID), getTask (get by ID), and updateTask (retrieve, modify fields, put back). Handle edge cases like missing IDs and duplicate IDs.

4. Analyze Trade-offs

Discuss time and space complexity: O(1) average for operations, O(n) space. Compare with other structures (e.g., balanced BST for ordered access, linked list for insertion order). Mention concurrency trade-offs (locking vs. lock-free).

5. Extend and Optimize

Propose enhancements like secondary indexes for querying by status or assignee, persistence via write-ahead log, or sharding for scalability. Discuss how these affect trade-offs.

Key Points to Mention

Time complexity: O(1) average for add, get, update with hash map; worst-case O(n) with collisions.
Space complexity: O(n) for storing tasks; overhead of hash map entries.
Concurrency: use ConcurrentHashMap or synchronized blocks; discuss lock contention and scalability.
Ordering: hash map does not maintain insertion order; use LinkedHashMap if needed.
Alternative data structures: balanced BST (O(log n) operations, ordered), trie for ID prefixes, or database for persistence.
Edge cases: handling duplicate IDs, missing IDs, null values, and thread safety during updates.

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