Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

A 75-minute coding interview at OpenAI for a SWE role where the task was to design a custom data structure optimized for both reads and writes. No ML, no fancy algorithms, just engineering judgment and clean code. Felt more like a design conversation than a grind session.

Questions Asked (1)

Design and implement a custom data structure that is optimized for both read and write operations. Define a reasonable API, explain the trade-offs, and walk through working code with examples.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The open-endedness is what gets you.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data structure that balances read and write efficiency, such as a hybrid of a hash map and a linked list. Define a clear API, discuss trade-offs (time/space complexity, concurrency, persistence), and walk through a concrete implementation with examples.

Pro tip: Focus on the trade-offs and justify your design choices based on the problem context; interviewers value the reasoning process more than the final data structure.

1. Clarify Requirements

Ask questions to understand the expected operations, read/write ratio, data size, concurrency needs, and any constraints (e.g., memory, latency).

2. Define API and Operations

Specify the methods (e.g., insert, delete, get, update) and their expected time complexities, ensuring they align with the requirements.

3. Propose Data Structure and Trade-offs

Describe the chosen data structure (e.g., hash map + doubly linked list for LRU cache) and analyze trade-offs in time/space, simplicity, and scalability.

4. Implement and Walk Through Code

Write clean, working code for the core operations, explaining key parts and edge cases, and provide examples of usage.

5. Discuss Extensions and Optimizations

Mention potential improvements like concurrency handling, persistence, or alternative structures for different scenarios.

Key Points to Mention

Time and space complexity of each operation (e.g., O(1) for reads/writes in a hash map).
Trade-offs between different data structures (e.g., arrays vs. linked lists for writes).
Concurrency considerations (e.g., locks, lock-free structures) if applicable.
Edge cases: empty structure, duplicate keys, resizing, and error handling.
Real-world examples where such a structure is used (e.g., LRU cache, database indexes).
Testing and validation: unit tests, performance benchmarks.

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