Scale and Complexity Experience Questions
Experience supporting or building large scale systems and complex enterprise environments including high traffic applications, distributed systems, global operations, incident patterns, and operational trade offs. Candidates should be able to discuss scaling bottlenecks, observability strategies, capacity planning, and examples demonstrating handling complexity at product and infrastructure levels.
MediumTechnical
45 practiced
Multiple product teams compete for the same inference and training capacity. As the infra owner, describe a policy and operating model for fair resource allocation: quotas, priority tiers, escalation paths, cost attribution per team, noisy-neighbor mitigation, and enforcement mechanisms. How would you onboard teams and measure effectiveness?
EasyTechnical
51 practiced
Define SLOs, SLIs, and SLAs in the context of machine learning model serving. Provide concrete examples of SLIs appropriate for an online inference service (include latency, error rate, and model-quality metrics), and explain how SLOs should drive operational actions such as alerting thresholds, automatic rollbacks, and prioritization during incidents.
HardSystem Design
39 practiced
Design a serving architecture for very large transformer models that cannot fit on a single GPU (requiring model parallelism). Requirements: support 1000 RPS aggregate with 95th percentile latency under 200ms, allow batching, and support hot model reloads. Discuss model partitioning, RPC boundaries, pipeline parallelism vs tensor parallelism, and failure handling.
HardTechnical
50 practiced
Compliance restricts logging of raw feature values for certain PII features. How would you design an observability system that still detects distributional drift and anomalies for models that rely on those features while preserving privacy? Consider aggregated statistics, hashing, quantile sketches, histograms, and differential privacy techniques.
HardSystem Design
47 practiced
Architect a global, personalized ML serving platform for a social product with 200M monthly active users. Requirements: sub-100ms median inference latency globally, strict country-level data residency, per-user personalization state updated in real time, and ability to run region-specific models. Explain routing, model placement, feature access, state synchronization, and how to meet SLOs while complying with regulations.
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