Openai·Software Engineer·Onsite - Coding / Algorithms·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

OpenAI software engineer coding round with one problem that took most of the session. The last test case never passed but the interviewer wrapped things up early anyway after I walked through my logic, which was a bit of a relief and also kind of anticlimactic.

Questions Asked (1)

Implement a follower/snapshot/recommendation system (a problem specific to this company's interview pool).

Algorithms & Data StructuresSystem Design

Author's notes

Finished the main logic in about 35 minutes which felt decent, but then got stuck on one edge case in the final test and just could not get it to pass.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and requirements, then design a scalable system that handles follower relationships, snapshot generation, and personalized recommendations. Discuss trade-offs between different architectural choices and algorithms, and outline how to handle data consistency and real-time updates.

Pro tip: Emphasize the importance of defining clear SLAs and data freshness requirements early, as they drive key design decisions like batch vs. stream processing and storage choices. Show awareness of OpenAI's scale and the need for efficient, low-latency recommendations.

1. Clarify Requirements and Scope

Ask questions to understand functional and non-functional requirements: expected scale (users, follows, requests per second), latency and freshness needs, and what 'snapshot' and 'recommendation' mean in this context. Define the core entities and operations.

2. High-Level Architecture

Sketch a scalable architecture: separate services for follower graph, snapshot generation, and recommendation engine. Choose appropriate data stores (e.g., graph DB for follows, columnar store for snapshots, cache for recommendations) and discuss data flow.

3. Follower/Snapshot Design

Detail how to store and query follower relationships efficiently (e.g., adjacency lists, sharding). Explain snapshot generation: when to take snapshots (e.g., periodic, on-demand), how to store them, and how to serve them with low latency.

4. Recommendation Algorithm

Propose a recommendation approach (e.g., collaborative filtering, content-based, graph-based) suitable for the scale. Discuss how to generate recommendations from snapshots and follower data, and how to update them incrementally.

5. Scalability, Consistency, and Trade-offs

Address scaling challenges: partitioning, replication, caching, and handling hot users. Discuss consistency models (eventual vs. strong) and trade-offs between latency, cost, and freshness. Mention monitoring and failure handling.

Key Points to Mention

Data modeling for follower relationships (e.g., adjacency lists, graph databases, sharding by user ID)
Snapshot generation strategies (batch processing, incremental updates, versioning, and storage formats)
Recommendation algorithms (collaborative filtering, matrix factorization, graph-based methods) and their scalability
Caching and serving layer for low-latency recommendations (e.g., Redis, CDN)
Handling scale: partitioning, replication, and load balancing for hot users
Consistency and freshness trade-offs (eventual consistency, real-time vs. batch updates)

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