Fellow GPU Performance Optimization Engineer

About the role

The Role 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.

Responsibilities

• Lead performance optimization of large-scale AI training workloads on AMD GPU platforms across single-node and multi-node environments.
• Identify and eliminate system bottlenecks across compute, memory, and communication (e.g., kernel efficiency, memory bandwidth, network utilization).
• Optimize distributed training strategies (Data, Tensor, Pipeline Parallelism, ZeRO, etc.) for scalability and efficiency on AMD hardware.
• Drive cross-stack optimizations spanning kernels, compilers, runtimes, communication libraries, and ML frameworks.
• Develop and apply advanced profiling, benchmarking, and performance modeling methodologies.
• Collaborate with hardware, compiler, and framework teams to influence next-generation GPU architecture and software stack design.
• Contribute to and lead open-source efforts to improve ecosystem performance on AMD platforms.
• Define best practices and guide teams on performance tuning for large-scale training workloads.
• Stay at the forefront of advancements in large-scale training systems and performance optimization techniques.

Requirements

• Deep expertise in GPU architecture and performance characteristics (compute units, memory hierarchy, interconnects such as PCIe/Infinity Fabric/RDMA).
• Strong experience with performance profiling tools (e.g., ROCm tools, Nsight-like systems, custom profilers) and bottleneck analysis.
• Proven experience optimizing large-scale distributed training workloads across thousands of GPUs.
• Experience with distributed training frameworks such as Megatron-LM, Torchtitan, MaxText, or equivalent.
• Strong understanding of communication libraries and patterns (e.g., NCCL/RCCL, collective ops, overlap of compute and communication).
• Expertise in ML frameworks (PyTorch, JAX, TensorFlow) with a focus on performance tuning.
• Proficiency in Python and at least one systems language (C++/CUDA/HIP), including debugging and low-level optimization.
• Experience with compiler stacks, kernel optimization, or graph-level optimization is a strong plus.

Preferred Experience

• Demonstrated technical leadership and ability to influence cross-functional teams.
• Ph.D. in Computer Science, Computer Engineering, or a related field preferred, or equivalent industry experience with significant technical impact.

LOCATION: San Jose, CA

This role is not eligible for visa sponsorship.