AI Systems Performance Engineer

Advanced Micro Devices, Inc•Austin, TX

1d•Hybrid

About The Position

As a member of AMD's AI Systems Power and Performance Engineering team, you will play a critical role in evaluating and optimizing the performance of next-generation AI workloads on AMD embedded and client platforms. Working at the intersection of AI software, hardware architecture, and system performance, you will help deliver data-driven insights that influence product development, competitive positioning, and customer success. You will collaborate with AI software teams, architects, firmware developers, platform engineers, product planners, and business stakeholders to characterize AI workloads, identify performance bottlenecks, and drive continuous improvements across AMD's AI-enabled products.

Requirements

Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field.
Strong Linux systems knowledge and experience working in command-line environments.
Experience with performance profiling, workload tracing, and system analysis tools.
Understanding of CPU, GPU, NPU, memory, and system-level performance interactions.
Experience with Python, Shell scripting, or automation frameworks.
Understanding of power and performance optimization methodologies.

Nice To Haves

Experience with AI inference frameworks such as PyTorch, ONNX Runtime, TensorFlow, vLLM, llama.cpp, or similar technologies.
Familiarity with modern AI model architectures including LLMs, VLMs, CNNs, and Vision Transformer (ViT) workloads.
Knowledge of AMD, x86, ARM, or embedded computing platforms is highly desirable.
Familiarity with BIOS configuration and system bring-up activities is a plus.

Responsibilities

Benchmark and characterize AI inference workloads across AMD embedded and client platforms.
Execute performance, power, and efficiency analysis of modern AI workloads, including Large Language Models (LLMs), Vision Language Models (VLMs), computer vision applications, and emerging AI use cases.
Develop and maintain automated benchmarking environments and performance dashboards.
Configure and optimize AI workload execution environments using industry-standard frameworks and runtimes.
Collect, analyze, and interpret system-level performance traces using hardware and software profiling tools.
Investigate workload bottlenecks involving compute, memory bandwidth, latency, and system resource utilization.
Work closely with software, firmware, architecture, and platform teams to support performance analysis and optimization activities.
Support the development of benchmarking methodologies used for competitive analysis and product evaluation.
Generate data-driven reports and present findings to engineering stakeholders.