Lead DCGPU Performance Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING   At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.   Together, we advance your career.    Lead DCGPU Performance Engineer   THE ROLE: AMD is looking for an outstanding technical contributor to drive performance measurement and characterization of Data Center GPU (DCGPU) systems for AI workloads. This role focuses on producing accurate, repeatable, and trustworthy performance data across a wide range of AI workloads, platforms, and configurations. The engineer will establish robust measurement methodologies and ensure consistency across environments, enabling reliable performance insights for engineering, product, and business decisions. THE PERSON: As a highly detail-oriented and data-driven DCGPU Performance Engineer, you will specialize in performance measurement at system and workload levels, ensuring that results are reproducible, comparable, and representative of real-world behavior. You will define and enforce best practices for performance measurement across AI workloads, including training and inference, while accounting for system variability, configuration differences, and evolving software stacks. You are expected to become an expert user of internal performance tools and workflows, enabling efficient data collection and high-quality reporting. The ideal candidate combines deep technical expertise with a strong sense of rigor and discipline in experimentation, validation, and reporting. You are expected to question results, validate assumptions, and continuously improve measurement infrastructure through close collaboration with tools teams.

Performance Measurement & Characterization Measure performance of DCGPU systems across AI workloads (training, inference, microbenchmarks) Ensure accurate capture of key metrics such as throughput, latency, efficiency, and scaling behavior Validate performance across different system configurations, software stacks, and runtime environments Reproducibility & Methodology Define and enforce best practices for reproducible performance measurement Ensure experiments are repeatable across systems, teams, and time Establish controls for variables such as software versions, system configuration, and workload parameters Develop standardized methodologies for fair and consistent comparisons Benchmarking & Workload Execution Execute AI workloads (LLMs, training, inference) with well-defined configurations Ensure consistency in workload setup, execution, and reporting across runs Maintain benchmark definitions and configuration baselines Support internal and competitive benchmarking efforts Data Accuracy & Validation Cross-check results for anomalies, inconsistencies, and measurement errors Validate data using multiple methods (profiling tools, logs, counters, independent runs) Identify sources of measurement noise and variability and mitigate them Ensure published results are reliable, defensible, and aligned with methodology Tooling & Measurement Infrastructure Develop and enhance tools for performance measurement, logging, and reporting Build automation for workload execution, result collection, and validation Enable standardized output formats for consistent analysis and reporting Improve measurement workflows to increase efficiency and reliability Tools Expertise & Feedback Loop Become an expert user of internal