Sr. Software Engineer - AI Triton Communication - CA, San Jose, United States

Originally posted 7 August 2026 by the employer.

About the role

As a Sr.

What you'll do

• Design and develop native distributed communication and execution capabilities within the Triton AMDGPU backend for scalable multi-GPU execution.
• 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 for 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.

What you'll need

• Deep expertise in GPU architecture, compiler technologies, and distributed GPU systems.
• Proven experience optimizing workloads at multi-GPU scale.
• Experience working with the GPU runtime, communication stack, or compiler backend.
• Strong knowledge of GPU execution, memory hierarchy, and inter-GPU communication performance impact.
• Motivation to build native distributed execution and communication capabilities tightly integrated with the compiler and runtime to maximize scalability and hardware utilization.
• Ability to solve complex system-level performance challenges and deliver scalable, high-performance GPU infrastructure.

Nice to have

• 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.
• 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.

Skills: Triton compiler, GPU kernels, distributed execution, multi-GPU, ROCm