Senior GPU Performance Engineer - AI s

Position: Senior GPU Performance Engineer - AI Workloads
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About the Role We are seeking a GPU 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.

Key Responsibilities Profile and optimize large-scale AI workloads, including transformers, multimodal models, diffusion models, and recommender systems, across multi-node, multi-GPU systems.

Perform deep GPU kernel-level analysis to identify bottlenecks in compute pipelines, memory hierarchy, instruction execution, synchronization, and data movement.

Root-cause performance gaps using GPU performance counters, thread traces, ISA/assembly analysis, and targeted microbenchmarks, and deliver low-level kernel optimizations.

Collaborate with kernel, compiler, runtime, framework, and distributed systems teams to improve kernel performance, scheduling, memory utilization, and system-level efficiency.

Develop and maintain benchmarks, microbenchmarks, and traces representative of foundation model and broader AI workloads.

Provide kernel- and microarchitecture-level performance insights to AMD Instinct GPU architecture teams, informing hardware/software co-design decisions for future architectures.

Present findings to cross-functional teams and leadership, shaping both software and hardware roadmaps.

Minimum Qualifications MS or PhD in Computer Science, Computer Engineering, or a related technical field.

Strong hands-on experience in GPU kernel-level performance analysis and low-level optimization using CUDA, HIP/ROCm, or equivalent technologies.

Deep understanding of GPU microarchitecture, including execution pipelines, SIMD/SIMT scheduling, registers, memory hierarchy, caches, and interconnects.

Experience using performance counters, thread traces, profilers, and ISA/assembly analysis to root-cause GPU kernel performance bottlenecks.

Hands-on experience optimizing performance-critical GPU kernels and developing targeted microbenchmarks or reproducer kernels.

Proficiency in C++ and GPU kernel programming, with Python experience for performance analysis and automation.

Advanced Linux OS, container technologies such as Docker, and Git/Git Hub skills.

Familiarity with AI workloads, AI frameworks, distributed computing, and communication libraries, such as PyTorch, JAX, Megatron-LM, NCCL/RCCL, and MPI.

Preferred Qualifications Strong track record of delivering GPU kernel performance improvements through low-level hardware/software analysis and optimization.

Experience working across kernel, compiler/runtime, framework, distributed systems, and GPU architecture teams to resolve complex performance bottlenecks.

Experience analyzing large-scale AI workloads and translating workload-level performance gaps into actionable kernel, software-stack, or…