Openai·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

OpenAI software engineering interview that was basically one big design problem: build a GPU credit manager from scratch with expiry logic, atomicity, and clean complexity guarantees. The kind of question where you can tell pretty quickly if you've thought about resource scheduling before or not.

Questions Asked (1)

Design and implement a GPU credit manager where users have grants with expiration timestamps. It needs to support adding grants, consuming credits using earliest-expiry-first ordering, checking balance at a given time, and optionally refunding the most recently consumed credits. All operations should run in O(log n) time, and consuming more than the available balance should leave state unchanged.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This is a meaty one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure like a balanced BST or min-heap keyed by expiration, combined with a stack for refunds. Explain how each operation achieves O(log n) time, handle edge cases like insufficient balance, and discuss trade-offs between different implementations.

Pro tip: Emphasize that consuming more than available must be atomic and leave state unchanged—demonstrate this by checking total balance first or using a two-phase approach. Also, mention that refunds require tracking consumption history, which adds a stack but maintains O(log n) for other operations.

1. Clarify Requirements and Constraints

Ask questions to confirm assumptions: Are grants per-user? Is time monotonic? Should refunds be LIFO? What are the exact O(log n) requirements for each operation?

2. Choose Data Structures

Propose a balanced BST (e.g., TreeMap) keyed by expiration for grants, and a stack for refunds. Alternatively, a min-heap with lazy deletion could work, but BST offers ordered traversal and efficient deletion.

3. Design Operations

Detail add_grant (insert into BST), consume (iterate earliest-expiry, deduct, push to stack), balance_at_time (sum unexpired grants), and refund (pop from stack and reinsert). Ensure consume checks total balance first to avoid partial consumption.

4. Analyze Complexity and Edge Cases

Show each operation is O(log n) amortized, except balance_at_time which may be O(k) if summing k grants; discuss optimization like maintaining a running total. Handle edge cases: no grants, expired grants, refund with empty stack.

5. Discuss Trade-offs and Extensions

Compare BST vs heap: BST allows ordered iteration and easy refunds; heap is simpler but refunds require reinsertion. Mention concurrency, persistence, and scaling to multiple users if relevant.

Key Points to Mention

Use a balanced BST (e.g., TreeMap) keyed by expiration timestamp to support earliest-expiry-first consumption and O(log n) insert/delete.
Maintain a stack of consumed grants for LIFO refunds, ensuring refund is O(log n) by reinserting into the BST.
For consume, first check if total available balance >= requested amount; if not, return failure without modifying state.
Track total available balance separately to make balance_at_time O(1) or O(log n) by subtracting expired grants lazily.
Handle expiration: grants with expiration <= current time are considered expired and should be removed or ignored.
Discuss trade-offs: BST vs min-heap, lazy vs eager expiration, and memory vs time for refund history.

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