Openai·Machine Learning Engineer·Technical Phone Screen·Senior

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Apr 2026

Summary

Interviewed for an MLE role at OpenAI and got a practical coding round where they asked me to build a key-value store from scratch. Not a LeetCode grind, more of a design-your-own-thing kind of problem which I wasn't fully prepared for.

Questions Asked (1)

Implement a simple key-value store from scratch.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with a basic hash map and they kept pushing on it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (operations, persistence, concurrency, scale) and then design a simple in-memory hash map with optional persistence and thread-safety. Discuss trade-offs like memory vs. disk, consistency vs. availability, and how it would integrate with ML workflows (e.g., feature store, caching).

Pro tip: Emphasize that for ML systems, the key-value store often needs to handle high-throughput reads for feature serving and support versioning or TTL for model artifacts; mentioning this shows you understand the domain.

1. Clarify Requirements

Ask about expected operations (get, put, delete), data size, persistence needs, concurrency, and latency/throughput requirements. This ensures you build the right thing.

2. Choose Data Structures

Select an in-memory hash map (e.g., Python dict) for O(1) average access. Discuss alternatives like balanced trees for ordered keys or LSM trees for write-heavy workloads.

3. Handle Persistence and Durability

Decide between in-memory only, write-ahead logging, or snapshotting. Explain trade-offs: speed vs. durability, and how to recover from crashes.

4. Address Concurrency and Scalability

Implement thread-safety with locks or use concurrent data structures. For scale, discuss sharding, replication, or using a distributed store like Redis.

5. Integrate with ML Workflows

Explain how the store supports ML use cases: caching features, storing model parameters, or serving embeddings with low latency. Mention TTL for stale data and versioning for models.

Key Points to Mention

Time complexity: O(1) average for get/put with hash map, worst-case O(n) with collisions.
Persistence options: write-ahead log (WAL) for durability, periodic snapshots for faster recovery.
Concurrency: use locks (e.g., threading.Lock) or concurrent hash maps; consider read-write locks for read-heavy workloads.
Scalability: sharding by key, replication for fault tolerance, and consistent hashing for distribution.
ML-specific considerations: TTL for feature freshness, versioning for model artifacts, and high-throughput reads for online serving.
Trade-offs: memory vs. disk, consistency vs. availability, and simplicity vs. performance.

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