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Optimize LLM Training and Serving

Last updated: Jun 22, 2026

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.

  • hard
  • Adobe
  • ML System Design
  • Machine Learning Engineer

Optimize LLM Training and Serving

Company: Adobe

Role: Machine Learning Engineer

Category: ML System Design

Difficulty: hard

Interview Round: Onsite

Quick Answer: 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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|Home/ML System Design/Adobe

Optimize LLM Training and Serving

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Adobe
May 25, 2026, 12:00 AM
hardMachine Learning EngineerOnsiteML System Design
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