How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Technical Screen rounds at Waymo.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Waymo during technical interviews.

Design Large-Scale Inference Serving

Last updated: May 23, 2026

Quick Overview

This question evaluates understanding of large-scale ML inference systems, assessing competencies in capacity planning, latency and tail-latency engineering, memory and bandwidth estimation, hardware selection (CPUs/GPUs/specialized accelerators), batching and caching trade-offs, and reliability concerns such as out-of-memory prevention and recovery. It is commonly asked to test practical system-design skills for production deployment by requiring back-of-the-envelope QPS and resource estimates and reasoning about operational trade-offs; this belongs to the ML system design category and emphasizes practical application over purely conceptual theory.

Waymo

Nov 27, 2025, 12:00 AM

Machine Learning Engineer

Technical Screen

ML System Design

Design a production inference serving system for a machine learning model used by 100 million daily active users. Your answer should cover: traffic assumptions and back-of-the-envelope QPS estimates; memory requirements for model weights, activations, caches, and batching; network and accelerator bandwidth estimates; how to choose CPUs, GPUs, or specialized accelerators; how to optimize latency and tail latency; and how to prevent or recover from out-of-memory failures.

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