Sr. Staff Distributed Systems Software Engineer, Collectives

Advanced Micro Devices, Inc • Austin, TX

21h

About The Position

As an experienced member of AMD’s Collectives and Network Performance team, the engineer will optimize collective communication and networking software for AMD GPUs. The role focuses on enhancing communication libraries and training and inference performance across multi-GPU and multi-node systems. The engineer will work with internal GPU library, runtime, and ROCm teams, as well as open-source maintainers, to integrate optimizations across multiple generations of AMD GPUs and a range of state-of-the-art AI models.

The role is suited to an experienced C++ developer with strong technical and analytical skills in Linux environments. The engineer is expected to take ownership of features and projects with limited guidance, contribute to defining scope and technical approaches, collaborate with a high-performing team, and support the growth of less experienced engineers. The position is based in Austin, Texas, or San Jose, California, and is not eligible for visa sponsorship.

Requirements

• Strong proficiency in C++ and Python, with experience in performance tuning, debugging, and test design.
• Strong technical and analytical skills in Linux environments.
• Solid knowledge of network, NIC, and GPU hardware architecture.
• Hands-on experience profiling and optimizing distributed AI workloads.
• Good understanding of modern training and inference optimization.
• Experience with large-scale, multi-node compute environments and optimizing AI workloads across clusters.
• Ability to take ownership of features and projects with limited guidance and contribute to defining scope and technical approach.
• Ability to collaborate effectively with cross-functional teams, mentor less experienced engineers, and communicate results clearly.
• Senior-level professional experience in technical software development, focused on performance engineering, communication libraries, networking, or GPU/distributed systems.
• Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or a Bachelor’s degree with equivalent experience.

Nice To Haves

• Strong knowledge of collective communication libraries such as RCCL, NCCL, or MPI.
• Good understanding of NIC, RDMA, and scale-up and scale-out network architectures.
• Hands-on experience integrating optimized compute or communication into ML frameworks such as PyTorch, JAX, vLLM, or SGLang, with a focus on throughput and scalability.
• PhD in a related field.

Responsibilities

• Design, implement, and optimize collective communication library features across scale-up and scale-out networks.
• Profile, tune, and analyze LLM, diffusion, multimodal, RecSys, and generative AI workloads on single-node and multi-node systems, and recommend design tradeoffs.
• Drive assigned features and projects from design through delivery with limited guidance, including scoping, implementation, testing, and performance validation.
• Develop and improve tools and infrastructure for benchmarking, performance estimation, regression tracking, and reporting.
• Collaborate with internal GPU library, runtime, and ROCm teams and open-source maintainers to ensure code changes meet requirements and are integrated upstream.
• Contribute software performance data and analysis to discussions about future scale-up networks, NICs, and scale-out networks.
• Help customers and internal teams apply effective load balancing, workload scheduling, and model sharding strategies, and present results and recommendations.
• Apply sound software engineering principles to deliver robust, maintainable solutions.
• Mentor junior engineers through code reviews, knowledge sharing, and technical guidance.

What This Job Offers

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