Optimize LLM Training and Serving

Quick Overview

This question evaluates hardware-aware ML systems engineering skills, specifically reasoning about memory-versus-compute bottlenecks and attention matrix materialization costs, interpreting HFU versus MFU metrics, and using GPU profiling signals to diagnose training and low-latency serving performance within the ML System Design domain.

Optimize LLM Training and Serving

Company: Adobe

Role: Machine Learning Engineer

Category: ML System Design

Difficulty: hard

Interview Round: Onsite

Overview: This question evaluates hardware-aware ML systems engineering skills, specifically reasoning about memory-versus-compute bottlenecks and attention matrix materialization costs, interpreting HFU versus MFU metrics, and using GPU profiling signals to diagnose training and low-latency serving performance within the ML System Design domain.

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Adobe
May 25, 2026
hardMachine Learning EngineerOnsiteML System Design
29
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