OpenAI Software Engineer Onsite Coding Questions

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I've been preparing for open AI interviews recently and have summarized most of the frequently asked questions I've seen. Generally speaking, it is very fragmented, so I summarize it to save everyone

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## Round 1 - Coding ## Problem **Format:** 5-round virtual onsite. Rounds include coding, system design, and behavioral. **Coding Round 1:** Given a large log file of API requests, each line formatted as `"TIMESTAMP METHOD PATH STATUS_CODE LATENCY_MS"`, parse and compute: - P50, P90, P99 latency per endpoint - Count of 5xx errors per endpoint per hour **Coding Round 2:** Design an in-memory task scheduler. Tasks have an ID, a scheduled timestamp, and a callback. Implement: - `schedule(task_id, run_at, callback)` - `cancel(task_id)` - `tick(current_time)` - fires all tasks due at or before `current_time` **System Design:** Design a distributed inference serving system for a large language model. Cover: - Load balancing across GPU replicas - Request batching for throughput - Handling streaming responses (token-by-token) - Model versioning and rollout **Behavioral Focus:** - A time you disagreed with a technical decision and how you handled it. - How you've influenced a team to adopt a better engineering practice. ## Follow-ups 1. For the log parser: how do you compute percentiles efficiently without sorting the full dataset? 2. For the task scheduler: what data structure gives O(log n) insert and O(log n) extraction of the earliest task? 3. In the serving system: how do you handle a GPU replica that goes down mid-request? 4. How would you test the task scheduler for correctness across time zone boundaries?

## Implement sendAsyncMessage and an End-to-End Simulation Previously, `sendAsyncMessage(nodeId, message)` was assumed to be provided. Now implement a runnable single-machine simulation. ### Tasks 1

## Reconstruct Distributed Tree Topology (Async Messaging) Same distributed tree and communication model as before: - No direct access to the global structure; only `sendAsyncMessage(nodeId, message

## Count Nodes in a Distributed Tree (Async Messaging) You are given a directed tree rooted at `root`. Each node represents an independent machine: - You **cannot** directly access `node.children` o

## Coding: Implement an extensible ChatApp with bots (multi-bot extensibility; multi-channel support) Implement a `ChatApp` class that processes user messages and bot responses. ### Requirements 1.

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Implement a simplified Multi-Head Attention forward pass using NumPy or PyTorch, and clearly state tensor shapes. ### Inputs - `Q, K, V`: each of shape `(B, T, D_model)` - number of heads `H` with `D