Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Got a coding question for a Software Engineer role at OpenAI that was more about careful bookkeeping than clever algorithms. The problem looked simple at first glance but the out-of-order timestamps and tie-breaking rules made it trickier than expected.

Questions Asked (1)

Design a GPU credit ledger class that supports adding credits, charging credits, and querying the balance at any given timestamp. Events can arrive out of order, and the balance at a given time must reflect all events up to that timestamp processed in chronological order, with charges only succeeding if the balance is sufficient at the time they're processed.

Algorithms & Data StructuresSystem DesignData Modeling

Author's notes

The out-of-order timestamp part is what got me initially.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: events can arrive out of order, so you need to store events and process them in timestamp order for queries. Design a class that maintains a sorted list of events (or a balanced BST) and computes balance on demand by replaying events up to the query timestamp, ensuring charges only succeed if sufficient balance exists at that point. Discuss trade-offs between eager and lazy processing, and consider using a Fenwick tree or segment tree for efficient range queries if needed.

Pro tip: Mention that you would use a self-balancing BST (like a TreeMap) to store events keyed by timestamp, and for each query, iterate through events in order, maintaining a running balance. This shows you understand the need for ordered processing and can handle out-of-order arrivals without sorting the entire dataset each time.

1. Clarify requirements and constraints

Ask about expected event volume, query frequency, and whether timestamps are unique. Confirm that charges must be validated against the balance at their timestamp, and that events can arrive in any order.

2. Choose data structures

Propose storing events in a balanced BST (e.g., TreeMap) keyed by timestamp to maintain chronological order. For efficient balance queries, consider augmenting with a Fenwick tree or segment tree to compute cumulative sums, or use a sorted list with binary search.

3. Design the class interface

Define methods: addCredit(timestamp, amount), charge(timestamp, amount) returning success/failure, and getBalance(timestamp). Explain that charge must simulate processing all events up to that timestamp in order to determine if the charge succeeds.

4. Handle out-of-order events and charge validation

Describe how to process events in timestamp order for each query or charge. For charge, replay events up to the charge timestamp, maintaining a running balance, and only apply the charge if balance >= amount. If not, the charge fails and is not recorded.

5. Analyze complexity and optimizations

Discuss time complexity: O(log n) for insertion, O(k) for query where k is number of events up to timestamp, or O(log n) with augmented trees. Mention potential optimizations like caching balances at checkpoints or using a segment tree with lazy propagation.

Key Points to Mention

Use of a balanced BST (e.g., TreeMap) to store events by timestamp for ordered processing.
Charge validation requires replaying events up to the charge timestamp to ensure sufficient balance at that moment.
Out-of-order events are handled by storing all events and sorting/processing on demand, not by assuming arrival order.
Trade-offs between eager (precompute balances) and lazy (compute on query) approaches.
Complexity analysis: O(log n) insertion, O(k) query, and possible O(log n) with augmented data structures.
Edge cases: duplicate timestamps, negative balances, and charges that fail due to insufficient funds.

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