Netflix ML Interview: Performance Optimization

Overview

You will be asked to optimize machine learning systems for production: reduce inference latency, lower cost, and scale reliably. The question focuses on both architectural choices (serving patterns, caching, sharding) and model-level techniques (quantization, pruning, distillation, hardware acceleration). You should show how to balance latency, throughput, accuracy, and cost in a real-world streaming or personalization use case.

Flow / Interview Stages

You’ll typically walk through: (1) defining SLOs and constraints (latency P99, throughput, memory, cost); (2) selecting a serving pattern (online vs batch vs hybrid) and storage/feature strategies (feature store, caching, sharding); (3) model optimization options (quantization, pruning, distillation, operator fusion, mixed precision, LoRA) and suitable hardware (CPU, GPU, inference accelerators); (4) scaling and rollout (horizontal autoscaling, load balancing, canary/A-B testing); (5) monitoring and observability (tail latency, error budgets, resource metrics, drift detection).

Skills You Need to Demonstrate

You should be comfortable with model serving architectures, inference optimization techniques, distributed scaling strategies, and performance monitoring. Be prepared to discuss trade-offs (accuracy vs latency, vertical vs horizontal scaling, cold-start vs warm caches) and to justify design choices with metrics and cost-benefit reasoning. Familiarity with modern efficient model families (Transformer variants, quantized LLMs, LoRA) and practical deployment patterns is a plus.