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AMD
San Jose, California
Source: AMD careers · View original posting
From AMD's posting. “We” and “our” refer to the employer.
We are seeking a Fellow GPU Performance Optimization Engineer to join our Models and Applications team. This role focuses on maximizing performance and efficiency of large-scale AI training workloads on AMD GPU platforms. You will drive innovations across the full software-hardware stack, optimizing distributed training at scale and pushing the limits of system throughput, scalability, and utilization for generative AI workloads.
This position requires deep expertise in GPU performance analysis, distributed systems, and ML workloads, along with the ability to influence architecture, software ecosystems, and best practices across the organization.
The ideal candidate is a recognized technical leader with deep expertise in GPU performance optimization, large-scale distributed training, and system-level bottleneck analysis. You have a strong understanding of GPU architecture, interconnects, memory hierarchies, and communication patterns, and can translate this knowledge into measurable improvements in training efficiency at scale.
You are comfortable operating across layers—from kernels and runtimes to frameworks and distributed strategies—and have a track record of driving impactful optimizations and influencing technical direction.
We are seeking a Fellow GPU Performance Optimization Engineer to join our Models and Applications team. This role focuses on maximizing performance and efficiency of large-scale AI training workloads on AMD GPU platforms. You will drive innovations across the full software-hardware stack, optimizing distributed training at scale and pushing the limits of system throughput, scalability, and utilization for generative AI workloads.
This position requires deep expertise in GPU performance analysis, distributed systems, and ML workloads, along with the ability to influence architecture, software ecosystems, and best practices across the organization.
The ideal candidate is a recognized technical leader with deep expertise in GPU performance optimization, large-scale distributed training, and system-level bottleneck analysis. You have a strong understanding of GPU architecture, interconnects, memory hierarchies, and communication patterns, and can translate this knowledge into measurable improvements in training efficiency at scale.
You are comfortable operating across layers—from kernels and runtimes to frameworks and distributed strategies—and have a track record of driving impactful optimizations and influencing technical direction.
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