Distributed Systems Software Engineer, Collectives

Advanced Micro Devices, Inc • San Jose, CA

1d • Onsite

About The Position

As a member of AMD’s Collectives and Network Performance team, the Distributed Systems Software Engineer contributes to developing, testing, and optimizing collective communication and networking software for AMD GPUs. The role supports improved training and inference performance across multi-GPU and multi-node systems, working with senior engineers and internal GPU library, runtime, and ROCm teams. The engineer learns to integrate optimizations across AMD GPU generations and AI models.

The position is suited to a motivated C++ developer with a foundation in Linux environments, computer architecture, and networking concepts. The role involves completing defined tasks with guidance, profiling and debugging performance issues, and collaborating with a team. The engineer builds expertise in collectives, networking, and GPU performance through mentorship, code reviews, knowledge sharing, and project work.

The position is onsite in San Jose, California, or Austin, Texas, and is not eligible for visa sponsorship. AMD notes that benefits are described in its “AMD benefits at a glance” information.

Requirements

• Proficiency in C++ and Python, with experience debugging, profiling, and writing tests.
• A foundational understanding of Linux environments, computer architecture, and networking concepts.
• A Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.
• Strong problem-solving skills, eagerness to learn, and good written and verbal communication skills.
• Professional experience in software development, with exposure to performance engineering, systems software, networking, or GPU programming.

Nice To Haves

• Understanding of collective communication concepts such as all-reduce and all-to-all.
• Familiarity with libraries such as RCCL, NCCL, or MPI and networking fundamentals such as RDMA, NICs, and interconnects.
• Foundational knowledge of GPU architecture; experience with HIP, CUDA, or similar GPU programming is a plus.
• Familiarity with AI frameworks such as PyTorch, JAX, vLLM, or SGLang and with distributed training or inference concepts.

Responsibilities

• Implement, test, and debug features in collective communication libraries and network software under the guidance of senior engineers.
• Profile LLM, diffusion, multimodal, RecSys, and generative AI workloads on single-node and multi-node systems and help identify performance bottlenecks.
• Run performance experiments across AMD GPU generations and network configurations, and summarize results for the team.
• Contribute to tools and infrastructure for performance measurement, regression tracking, and reporting.
• Support new model enablement and out-of-box performance efforts across AI frameworks, and work with internal teams and open-source communities to have changes reviewed and integrated.
• Follow sound software engineering practices, including code reviews, testing, and documentation, to deliver robust, maintainable code.
• Develop expertise in collectives, networking, and GPU performance through mentorship, code reviews, knowledge sharing, and hands-on project work.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level