> Markdown version of [/jobs/ext/4167339-distributed-systems-software-engineer-collectives](https://www.wearedevelopers.com/jobs/ext/4167339-distributed-systems-software-engineer-collectives). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Distributed Systems Software Engineer, Collectives

- **Company:** Advanced Micro Devices, Inc.
- **Location:** San Jose, CA, United States
- **Salary:** $172,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, C++ (Programming Language), Profiling, Code Review, Nvidia CUDA, Computer Networks, Computer Engineering, Software Debugging, Distributed Computing Environment, Distributed Systems, Network Interface Controllers, Python (Programming Language), Linux System Administration, Machine Learning, Networking Basics, Open Source Technology, Remote Direct Memory Access, Recommender Systems, Software Construction, Software Engineering, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Gpu Programming, Information Technology, SGLang, ROCm, Stable Diffusion
- **Published:** October 31, 2026
- **Apply:** https://diversityjobs.com/career/18796131/Distributed-Systems-Software-Engineer-Collectives-California-San-Jose

## About the Role

Seeking a motivated C++ developer with a solid foundation in Linux environments, computer architecture and networking concepts. The ideal candidate completes well-defined tasks with guidance, learns quickly, and collaborates effectively within a high-performing team. You are comfortable profiling and debugging performance issues and are interested in how AI model architectures map onto software and hardware. Familiarity with GPU programming and distributed training or inference is a plus., * Collective Communication & Network Fundamentals: Understanding of collective communication concepts (e.g., all-reduce, all-to-all), and familiarity with libraries such as RCCL, NCCL or MPI and with networking fundamentals such as RDMA, NICs and interconnects.

 * GPU Programming & Architecture: Foundational knowledge of GPU architecture; experience with HIP, CUDA or similar GPU programming is a plus.
 * Deep Learning Frameworks: Familiarity with AI frameworks such as PyTorch, JAX, vLLM or SGLang and with distributed training or inference concepts.
 * Software Engineering Fundamentals: Proficiency in C++ and Python, with experience in debugging, profiling and writing tests.
 * Problem Solving & Communication: Strong problem-solving skills, eagerness to learn, and good written and verbal communication skills.
 * Industry Experience:professional experience in software development, with exposure to performance engineering, systems software, networking, or GPU programming., * Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent

## Description

As a member of the Collectives and Network Performance team, you will contribute to developing, testing and optimizing collective communication and networking software for AMD GPUs. You will help improve training and inference performance across multi-GPU and multi-node systems, working with senior engineers and with internal GPU library, runtime and ROCm teams, and learning how to integrate optimizations across AMD GPU generations and a range of AI models., * Develop & Test Features: Implement, test and debug features in collective communication libraries and network software, under the guidance of senior engineers.

 * Profile & Analyze Workloads: Profile LLM, diffusion, multimodal, RecSys and generative AI workloads on single-node and multi-node systems, and help identify performance bottlenecks.
 * Run Benchmarks & Experiments: Run performance experiments across AMD GPU generations and network configurations, and summarize results for the team.
 * Contribute to Tooling: Contribute to tools and infrastructure for performance measurement, regression tracking and reporting.
 * Support Model & Framework Enablement: Support new model enablement and out-of-box performance efforts across AI frameworks, and work with internal teams and open-source communities to get changes reviewed and integrated.
 * Software Engineering Best Practices: Follow sound engineering practices, including code reviews, testing and documentation, to deliver robust, maintainable code.
 * Learn & Grow: Build expertise in collectives, networking and GPU performance through mentorship, code reviews, knowledge sharing and hands-on project work.
*, AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.