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Sr. Software Engineer - AI Triton Communication

AMD· Engineering
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San Jose, CA$164kFull-timePosted 2mo ago

About the role

Senior role focused on advancing Triton distributed execution and communication for AMD GPUs, improving compiler/runtime integration, and enabling scalable multi-GPU AI workloads on AMD Instinct accelerators.

Responsibilities

  • Design and develop native distributed communication and execution capabilities within the Triton AMDGPU backend, enabling scalable multi-GPU execution for large-scale AI workloads
  • Design and implement Triton compiler and runtime mechanisms for native GPU-initiated communication, including collective operations, remote memory access, synchronization, and distributed execution primitives
  • Drive performance optimization across compute and communication, including inter-GPU data movement, communication/computation overlap, memory hierarchy utilization, and GPU-driven scheduling efficiency
  • Develop and optimize distributed Triton kernels and execution models to achieve high performance, scalability, and efficient hardware utilization for AI workloads
  • Analyze, profile and debug complex cross-stack issues spanning Triton compiler, runtime, ROCm stack, and GPU hardware execution
  • Collaborate closely with GPU architecture, compiler, runtime, and performance teams to co-design and enable next-generation distributed GPU programming and execution capabilities
  • Contribute to open-source Triton and ROCm distributed ecosystem, driving innovation in distributed GPU computing

Qualifications

  • Bachelor’s or Master’s Degree in Computer Engineering, Computer Science, Electrical Engineering or equivalent practical experience
  • Deep experience in compiler development, GPU software, distributed systems, or performance engineering
  • Familiarity or hands-on experience with Triton compiler and runtime
  • Deep understanding of modern GPU architectures, including execution model, memory hierarchy (LDS, L2, HBM), scheduling, occupancy, and hardware performance characteristics
  • Good understanding of GPU runtime systems, communication stacks, and multi-GPU interconnects such as XGMI, NVLink, PCIe, or InfiniBand and their performance implications
  • Familiarity with distributed GPU communication libraries such as RCCL, NCCL, NVSHMEM, rocSHMEM, or MPI and similar technologies
  • Experience developing, optimizing, and scaling workloads across multiple GPUs, including inter-GPU communication, synchronization, and communication/computation overlap
  • Strong experience with GPU programming using Triton, HIP, CUDA, or similar parallel programming environments
  • Strong knowledge of MLIR and/or LLVM internals
  • Experience profiling, debugging, and optimizing performance across compiler, runtime, and hardware layers
  • Familiarity with ROCm, HIP, CUDA, or similar GPU programming ecosystems, including performance profiling and optimization tools
  • Experience optimizing large-scale AI, machine learning or HPC workloads across multi-GPU systems
  • Experience contributing to open-source projects and working in collaborative, cross-functional engineering environments
  • Strong problem-solving, communication, and technical leadership skills

Skills

Required:GPU architectureCompiler developmentCUDA/HIP/TritonDistributed GPU programming
Nice to have:Triton

Benefits

  • AMD benefits at a glance (referenced in posting)
Last updated Oct 8, 2026. Interested? Apply on the company site.
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