Openai·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

System design round at OpenAI for a software engineer role. The core problem was building a social graph with snapshot/versioning support, which sounds manageable until you actually have to think through the space complexity tradeoffs.

Questions Asked (1)

Design a social network data structure that supports follow, unfollow, snapshot creation, and querying whether user A was following user B at a specific past snapshot. Discuss time and space complexity tradeoffs.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just copy the whole graph on every snap() call and I said it out loud before catching myself, which was a rough start.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a baseline design using a graph with adjacency lists and versioning. Discuss tradeoffs between naive snapshotting and persistent data structures, and optimize for the given operations.

Pro tip: Mention that snapshots can be implemented with copy-on-write or persistent data structures to achieve O(1) snapshot creation and O(log n) queries, but be prepared to discuss the space overhead and garbage collection implications.

1. Clarify Requirements and Scale

Ask about expected number of users, follow operations per second, snapshot frequency, and query patterns. This determines the appropriate data structures and tradeoffs.

2. Design Baseline Data Structure

Propose a graph representation (e.g., adjacency lists) with a version or timestamp for each follow edge. For snapshots, consider storing a copy of the graph or using a versioned approach.

3. Optimize for Snapshots and Queries

Introduce persistent data structures (e.g., persistent hash maps or balanced trees) to allow efficient snapshots and queries. Discuss how to handle follow/unfollow with versioning.

4. Analyze Time and Space Complexity

Compare the baseline and optimized approaches: snapshot creation O(1) vs O(n), query O(1) vs O(log n), and space O(n) vs O(n log n) or O(n) with path copying.

5. Discuss Tradeoffs and Extensions

Summarize tradeoffs between simplicity, performance, and memory. Mention possible extensions like distributed storage, caching, or compression of snapshots.

Key Points to Mention

Use of persistent data structures (e.g., persistent balanced BST, hash array mapped trie) for efficient snapshots.
Versioning or timestamping of follow edges to support historical queries.
Tradeoff between space and time: full snapshot copies vs. incremental versioning.
Handling of unfollow operations and ensuring correct historical state.
Scalability considerations: sharding, distributed snapshots, and consistency.
Query optimization: indexing by user and time for fast lookups.

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