Senior GPU Performance Engineer - AI Workloads

We are seeking aGPU Performance Engineer to optimize AI workloads and guide the evolution of next-generation AMD Instinct GPU architectures. In this role, you will work across the software and hardware stack to perform deep GPU kernel-level performance analysis, identify microarchitectural bottlenecks, and drive low-level kernel optimizations for foundation model workloads on large-scale GPU systems. You will collaborate with kernel, framework, distributed systems, compiler/runtime, and hardware architecture teams to ensure our GPUs deliver industry-leading AI performance today while shaping the designs of tomorrow., 1. Profile and optimize large-scale AI workloads, including transformers, multimodal models, diffusion models, and recommender systems, across multi-node, multi-GPU systems.

1. Perform deep GPU kernel-level analysis to identify bottlenecks in compute pipelines, memory hierarchy, instruction execution, synchronization, and data movement.
2. Root-cause performance gaps using GPU performance counters, thread traces, ISA/assembly analysis, and targeted microbenchmarks, and deliver low-level kernel optimizations.
3. Collaborate with kernel, compiler, runtime, framework, and distributed systems teams to improve kernel performance, scheduling, memory utilization, and system-level efficiency.
4. Develop and maintain benchmarks, microbenchmarks, and traces representative of foundation model and broader AI workloads.
5. Provide kernel- and microarchitecture-level performance insights to AMD Instinct GPU architecture teams, informing hardware/software co-design decisions for future architectures.
6. Present findings to cross-functional teams and leadership, shaping both software and hardware roadmaps. 8., AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.

1. MS or PhD in Computer Science, Computer Engineering, or a related technical field.
2. Strong hands-on experience in GPU kernel-level performance analysis and low-level optimization using CUDA, HIP/ROCm, or equivalent technologies.
3. Deep understanding of GPU microarchitecture, including execution pipelines, SIMD/SIMT scheduling, registers, memory hierarchy, caches, and interconnects.
4. Experience using performance counters, thread traces, profilers, and ISA/assembly analysis to root-cause GPU kernel performance bottlenecks.
5. Hands-on experience optimizing performance-critical GPU kernels and developing targeted microbenchmarks or reproducer kernels.
6. Proficiency in C++ and GPU kernel programming, with Python experience for performance analysis and automation.
7. Advanced Linux OS, container technologies such as Docker, and Git/GitHub skills.
8. Familiarity with AI workloads, AI frameworks, distributed computing, and communication libraries, such as PyTorch, JAX, Megatron-LM, NCCL/RCCL, and MPI. 9.

Preferred Qualifications

1. Strong track record of delivering GPU kernel performance improvements through low-level hardware/software analysis and optimization.
2. Experience working across kernel, compiler/runtime, framework, distributed systems, and GPU architecture teams to resolve complex performance bottlenecks.
3. Experience analyzing large-scale AI workloads and translating workload-level performance gaps into actionable kernel, software-stack, or microarchitecture improvements.

$204,000.00/Yr.-$306,000.00/Yr.

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