Covers both technical and organizational strategies for growing capacity, capability, and throughput. On the technical side this includes designing and evolving system architecture to handle increased traffic and data, performance tuning, partitioning and sharding, caching, capacity planning, observability and monitoring, automation, and managing technical debt and trade offs. On the organizational side this includes growing engineering headcount, hiring and onboarding practices, structuring teams and layers of ownership, splitting teams, introducing platform or shared services, improving engineering processes and effectiveness, mentoring and capability building, and aligning metrics and incentives. Candidates should be able to discuss concrete examples, metrics used to measure success, trade offs considered, timelines, coordination between product and infrastructure, and lessons learned.
HardSystem Design
0 practiced
Design a control plane (scheduler + API) for allocating training and inference jobs across a heterogeneous pool of accelerators (GPUs, TPUs, inference-optimized chips). Requirements: support preemption, job priorities, elastic scaling, cost-aware placement, and eviction policies. Include API surface, placement algorithm, and how you’d integrate with tenant quotas and billing.
MediumTechnical
0 practiced
Technical debt accumulates quickly in ML systems. Describe a practical plan to identify, prioritize, and pay down technical debt across models, training code, and serving infra. Include metrics you'd use to measure debt (e.g., test coverage, reproducibility index), an ownership model for debt, and how you'd balance new feature work vs debt remediation.
MediumTechnical
0 practiced
List and explain practical strategies to reduce inference cost for large models in production (quantization, knowledge distillation, pruning, batching, caching, dynamic routing). For each strategy, describe expected cost reduction ranges, impact on model quality, and operational complexities (e.g., retraining, validation, supported hardware).
MediumTechnical
0 practiced
Implement a simple server-side cache for embedding lookups in Python. Given a function get_embedding(text) that is expensive, write a wrapper with LRU eviction, TTL per key, and a thread-safe API usable by multiple worker threads. You may use standard library modules but not external caching libraries. Provide code sketch and explain how you'd scale this to multiple processes or machines.
MediumSystem Design
0 practiced
You need to serve nearest-neighbor vector search for a recommendation system with hundreds of millions of vectors and strict latency targets (<50ms). Describe how you'd scale ANN (approximate nearest neighbor) index shards, use quantization, caching hot shards, handle index updates, and coordinate between retrieval and downstream model components. Do not deep-dive into DB internals; focus on system-level architecture and trade-offs.
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