Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026

Summary

OpenAI software engineer interview with a system design coding question that was more implementation-heavy than I expected. Not a pure whiteboard theory session, they actually wanted working code.

Questions Asked (1)

Build an in-memory key-value store with set, get, shutdown (which flushes all data as bytes to a storage medium), and restore (which reloads that data back into memory).

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The set/get part took me about two minutes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design the in-memory data structure and the persistence format. Discuss trade-offs between simplicity and performance, and outline the shutdown and restore flows with attention to durability and consistency.

Pro tip: Mention that you would use an append-only log or snapshot with checksums to ensure data integrity, and that you'd consider atomic writes (write to temp file then rename) to avoid corruption during shutdown.

1. Clarify Requirements

Ask about expected data size, concurrency needs, persistence guarantees, and whether the store should support additional operations like delete or list.

2. Design In-Memory Structure

Choose a suitable data structure (e.g., hash map) and consider thread-safety if concurrent access is required. Discuss time complexity for set and get.

3. Design Persistence Format

Decide on a serialization format (e.g., JSON, binary) and storage medium (file, memory-mapped file). Consider compression, encryption, and versioning.

4. Implement Shutdown and Restore

Outline the shutdown process: serialize all data, write to storage atomically, and handle errors. For restore: read from storage, deserialize, and repopulate the in-memory store.

5. Discuss Trade-offs and Edge Cases

Address performance vs. durability, memory usage, failure scenarios (e.g., crash during shutdown), and potential optimizations like incremental snapshots.

Key Points to Mention

Choice of in-memory data structure (e.g., hash map) and its time complexity for set/get.
Serialization format and its impact on performance, size, and compatibility.
Atomicity and durability guarantees during shutdown (e.g., write-ahead log, fsync).
Error handling and recovery from partial writes or corrupted files.
Concurrency control if multiple threads access the store.
Scalability considerations for large datasets and potential need for sharding or compression.

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