GPU Software Development Eng

Job Description

Key Responsibilities

• Lead performance optimization of large-scale AI training/RL/inference 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).
• 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.
• Stay at the forefront of advancements in large-scale systems and performance optimization techniques.
  
  

Preferred Experience

• 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 frameworks such as Megatron-LM, Torchtitan, vLLM, Sglang, or equivalent.
• Strong understanding of communication libraries and patterns (e.g., NCCL/RCCL, collective ops, overlap of compute and communication).
• 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.
• Demonstrated technical leadership and ability to influence cross-functional team
  
  

Academic Credentials

• Masters or PhD or equivalent experience in Computer Science, Computer Engineering, or related field

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