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

SeniorPrefer not to say
May 2026

Summary

OpenAI coding round for a software engineer role, basically one meaty problem the whole time. The problem was interesting but the scope negotiation with the interviewer was a bit stressful since the requirements weren't fully defined upfront.

Questions Asked (1)

Q1

Design and implement a GPU credits accounting system that handles a stream of operations: granting credits with an expiration time, consuming credits for a user, and querying a user's current balance. Then write end-to-end tests covering the main scenarios.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The core logic wasn't the hard part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a data model that efficiently handles grants with expiration and consumption. Implement the system with appropriate data structures and algorithms, and finally write comprehensive end-to-end tests covering edge cases.

Pro tip: Discuss trade-offs between different data structures (e.g., priority queues vs. sorted lists) for managing expirations, and emphasize the importance of idempotency and concurrency in a real-world system.

1. Clarify Requirements

Ask questions to understand expected scale, concurrency, expiration semantics, and whether consumption should follow FIFO or another policy.

2. Design Data Model

Propose a data model for users, grants (with expiration), and consumption records, considering efficient querying and updates.

3. Implement Core Operations

Outline algorithms for granting credits, consuming credits (respecting expiration and balance), and querying balance, handling edge cases like insufficient credits.

4. Write End-to-End Tests

Design tests that cover granting, consuming, expiration, balance queries, and error scenarios, ensuring the system behaves correctly under various conditions.

5. Discuss Trade-offs and Scalability

Explain choices made, potential bottlenecks, and how the system could scale or handle concurrency, showing awareness of production concerns.

Key Points to Mention

  • Data structures for efficient expiration management (e.g., min-heap, time-ordered list)
  • Consumption policy (FIFO, LIFO, or earliest-expiry-first) and its implications
  • Handling of expired credits during consumption and balance queries
  • Concurrency control and idempotency for operations
  • Testing strategies: unit tests, integration tests, and edge cases
  • Scalability considerations: sharding, caching, and eventual consistency

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