Openai·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

Did a system design round for an ML Engineer role at OpenAI, focused on version control for systems. Not a ton of detail to share but it was a legitimate design round, not a coding screen.

Questions Asked (1)

Design a version control system for machine learning models and related system artifacts.

System DesignTechnical Trade-offsData Modeling

Author's notes

The content here is pretty sparse so I'm going off the round type.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a content-addressable storage system that versions models, datasets, code, and configurations as immutable artifacts with rich metadata. Emphasize trade-offs between storage efficiency, retrieval speed, and lineage tracking, and discuss how to integrate with existing ML workflows.

Pro tip: Highlight the importance of reproducibility and lineage: version not just model weights but also the exact training data, hyperparameters, and environment. Mention that deduplication and garbage collection are critical for cost control at scale.

1. Clarify Requirements and Scope

Ask about scale (number of models, size), access patterns (training, inference, auditing), and integration needs (CI/CD, experiment trackers). Define what artifacts to version: models, datasets, code, configs, metrics.

2. Design Core Storage and Versioning

Propose a content-addressable store (e.g., hash-based) for immutable artifacts, with a metadata layer for tags, lineage, and relationships. Discuss chunking and deduplication for large files.

3. Define Data Model and Metadata

Outline entities: Model, Version, Experiment, Dataset, Environment. Include metadata like author, timestamp, metrics, and parent-child relationships for lineage.

4. Address Scalability and Performance

Discuss distributed storage (e.g., S3), caching for frequent access, and indexing for fast queries. Consider trade-offs between consistency and availability.

5. Plan Integration and Lifecycle Management

Explain how to integrate with training pipelines, model serving, and CI/CD. Cover garbage collection, retention policies, and access control.

Key Points to Mention

Content-addressable storage and deduplication to handle large model files efficiently.
Lineage tracking to link models to datasets, code commits, and hyperparameters for reproducibility.
Metadata management: tags, annotations, and searchable attributes for discovery.
Trade-offs between storage cost, retrieval latency, and consistency.
Integration with existing ML tools (e.g., MLflow, Git, DVC) and APIs for programmatic access.
Security and access control: encryption, authentication, and audit logs.

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