OpenAI·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Got a pretty gnarly coding problem for an OpenAI software engineer round. The problem was a custom data structure with some real edge cases around ordering and retroactive updates. Came out feeling like I had a decent handle on the core design but probably fumbled the complexity analysis at the end.

Questions Asked (1)

Design and implement an expiring-credit ledger that supports out-of-order event insertion. The ledger should expose three operations: adding a credit bucket with an id, amount, activation timestamp, and expiration duration; charging a given amount at a given timestamp by consuming from the earliest-expiring active credits first (with tie-breaking by activation time then lexicographic id); and querying the total active balance at any timestamp. Events can arrive in arbitrary timestamp order, so the structure must handle retroactive inserts correctly.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The basic structure clicked pretty fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then propose a data structure that supports efficient insertion, charging, and querying under out-of-order events. Discuss trade-offs between different approaches (e.g., segment tree vs. balanced BST) and outline the algorithm for each operation, emphasizing correctness and complexity.

Pro tip: Mention that you would handle out-of-order events by using a data structure that maintains credits sorted by expiration, and that you would consider lazy deletion or time-based indexing to avoid reprocessing. Also, discuss how to handle edge cases like charging more than available balance or overlapping expirations.

1. Clarify Requirements and Assumptions

Ask clarifying questions about the expected scale, whether timestamps are integers or floats, and if concurrent operations are needed. Confirm that credits expire after activation + duration and that charging consumes from earliest-expiring first.

2. Choose Data Structures

Propose a data structure that maintains active credits ordered by expiration time, activation time, and id. Consider a balanced BST (e.g., TreeMap) or a segment tree over time for efficient range queries and updates.

3. Design Operations

For add: insert the credit into the structure, handling out-of-order by placing it correctly. For charge: traverse credits in order, deducting amounts until the charge is satisfied, removing expired or fully consumed credits. For query: sum active balances at the given timestamp, possibly using a prefix sum or range query.

4. Analyze Complexity and Trade-offs

Discuss time and space complexity for each operation. Compare alternatives like using a priority queue with lazy deletion vs. a segment tree, and explain why your choice is optimal for the given constraints.

5. Handle Edge Cases and Test

Consider edge cases: charging when insufficient balance, credits expiring exactly at the query timestamp, and out-of-order inserts that affect past charges. Walk through a small example to validate the design.

Key Points to Mention

Ordering by expiration time, then activation time, then lexicographic id for tie-breaking.
Handling out-of-order events by inserting credits into the correct position in the sorted structure.
Efficient charging by consuming from the earliest-expiring active credits, possibly using a pointer or iterator.
Querying total active balance at a timestamp, considering expiration and activation times.
Trade-offs between different data structures (e.g., balanced BST, segment tree, priority queue with lazy deletion).
Complexity analysis: O(log n) for insert and charge, O(log n) or O(1) for query depending on structure.

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