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# Sr. Staff Distributed Systems Software Engineer, Collectives

- **Company:** Advanced Micro Devices, Inc.
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Salary:** $174,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Profiling, Code Review, Computer Networks, Computer Engineering, Software Debugging, Distributed Computing Environment, Distributed Systems, Network Interface Controllers, Python (Programming Language), Linux System Administration, Machine Learning, Network Architecture, Node.Js, Open Source Technology, Performance Tuning, Remote Direct Memory Access, Recommender Systems, Inference Optimization, Tensorflow, Software Construction, Software Engineering, Graphics Processing Unit (GPU), Load Balancing, High Performance Computing, Performance Testing, Pytorch, Large Language Models, Deep Learning, Information Technology, SGLang, ROCm, Stable Diffusion, Software Performance
- **Published:** October 31, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88751725/1

## About the Role

Seeking an experienced C++ developer with strong technical and analytical skills in Linux environments. The ideal candidate can take ownership of features and projects with limited guidance, contribute to defining scope and technical approach, and collaborate effectively within a high-performing team. You have solid knowledge of network, NIC and GPU hardware architecture, hands-on experience profiling and optimizing distributed AI workloads, and a good understanding of modern training and inference optimization. You also support the growth of less experienced engineers., * Collective Communication & Network Knowledge: Strong knowledge of collective communication libraries (e.g., RCCL, NCCL, MPI) and a good understanding of NIC, RDMA and scale-up and scale-out network architectures.

 * Performance Analysis: Strong experience in profiling, performance analysis and bottleneck identification for distributed AI workloads.
 * Deep Learning Integration: 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.
 * Software Engineering Excellence: Strong proficiency in C++ and Python with solid experience in performance tuning, debugging and test design.
 * High-Performance Computing: Experience with large-scale, multi-node compute environments and optimizing AI workloads across clusters.
 * Collaboration & Communication: Ability to work effectively with cross-functional teams, mentor less experienced engineers, and communicate results clearly.
 * Industry Experience: senior level ofprofessional experience in technical software development, with a focus 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). A PhD is a plus.

## Description

As an experienced member of the Collectives and Network Performance team, you will play an important role in optimizing collective communication and networking software for AMD GPUs. You will enhance communication libraries and training/inference performance across multi-GPU and multi-node systems, working with internal GPU library, runtime and ROCm teams and with open-source maintainers to integrate optimizations across multiple generations of AMD GPUs and a range of state-of-the-art AI models., * Develop Collective Communication Features: Design, implement and optimize features in collective communication libraries across scale-up and scale-out networks.

 * Optimize Distributed Training & Inference: Profile, tune and analyze LLM, diffusion, multimodal, RecSys and generative AI workloads on single-node and multi-node systems, and recommend design tradeoffs.
 * Own Features End to End: Drive assigned features and projects from design through delivery with limited guidance, including scoping, implementation, testing and performance validation.
 * Performance Modeling & Tooling: Develop and improve tools and infrastructure for benchmarking, performance estimation, regression tracking and reporting.
 * Collaborate with Framework & Library Teams: Work with internal GPU library, runtime and ROCm teams, and with open-source maintainers, to ensure code changes meet requirements and are integrated upstream.
 * Hardware-Software Co-Design Input: Contribute software performance data and analysis to discussions on future scale-up networks, NICs and scale-out networks.
 * Customer & Stakeholder Support: Help customers and internal teams apply effective load balancing, workload scheduling and model sharding strategies, and present results and recommendations.
 * Software Engineering Best Practices: Apply sound engineering principles to deliver robust, maintainable solutions.
 * Guide & Mentor: Mentor junior engineers through code reviews, knowledge sharing and technical guidance., AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.