Design a model downloader
Company: Anthropic
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design a system that distributes machine learning model artifacts from centralized storage to a large fleet of inference servers.
The system should support:
- versioned model artifacts and metadata
- integrity validation using checksums or signatures
- efficient rollout to thousands of hosts without overwhelming storage or network bandwidth
- local caching on each host
- canary deployment, staged rollout, and fast rollback
- visibility into which model version is active on each host
- authentication, authorization, and auditability
- recovery from partial downloads, corrupted files, and failed activations
Describe the main components, host-side behavior, APIs, and scaling strategy.
Quick Answer: This question evaluates a candidate's competency in ML system design and distributed systems, covering model lifecycle management, versioning, integrity verification, efficient rollout, local caching, security, observability, and fault recovery.