Sr. Software Engineer - AI Triton Communication

Advanced Micro Devices, Inc•San Jose, CA

20h•Hybrid

About The Position

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them. AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger — technology that moves the world forward. THE ROLE: Triton is a widely adopted language and compiler for high-performance GPU kernels, powering major AI frameworks such as PyTorch, vLLM, and SGLang. As AI workloads increasingly scale across multiple GPUs and nodes, first-class support for distributed execution and communication in Triton is strategically critical to enabling efficient large-scale training and inference on AMD Instinct Accelerators. AMD GPUs are an official Triton backend, and delivering industry-leading distributed performance and scalability on AMD Instinct accelerators is a key priority. The performance, scalability, and usability of Triton directly impact the competitiveness of AMD hardware in large-scale AI deployments. In this role, you will advance the Triton compiler and runtime stack for AMD CDNA and next-generation GPU architectures by building native distributed execution and communication capabilities. You will develop compiler and runtime infrastructure that enables efficient inter-GPU communication, scalable execution, and optimal hardware utilization. You will work across compiler, runtime, and hardware layers, collaborating closely with GPU architecture and software teams to help establish AMD GPUs as a best-in-class platform for Triton-based distributed AI.

Requirements

Deep expertise in GPU architecture, compiler technologies, and distributed GPU systems, with proven experience optimizing workloads at multi-GPU scale.
Comfortable working across the full execution stack — from compiler and runtime to hardware — and understand how GPU execution, memory hierarchy, and inter-GPU communication impact performance.
Experience working close to the GPU runtime, communication stack, or compiler backend, and are motivated to build native distributed execution and communication capabilities tightly integrated with the compiler and runtime to maximize scalability and hardware utilization.
Thrive on solving complex system-level performance challenges and delivering scalable, high-performance GPU infrastructure.

Nice To Haves

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
Bachelor’s or Master's Degree in Computer Engineering, Computer Science, Electrical Engineering or equivalent practical experience

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