Sr. Staff Distributed Systems Software Engineer, Collectives

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THE ROLE:

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.

THE PERSON:

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.

KEY RESPONSIBILITIES:

• 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.

PREFERRED EXPERIENCE:

• 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 of professional experience in technical software development, with a focus on performance engineering, communication libraries, networking, or GPU/distributed systems.

ACADEMIC CREDENTIALS:

• 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.

LOCATION:

Austin, Texas or San Jose, California.

This role is not eligible for visa sponsorship.

#LI-MV1

Benefits offered are described: AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.

This posting is for an existing vacancy.

THE ROLE:

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.

THE PERSON:

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.

KEY RESPONSIBILITIES:

• 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.

PREFERRED EXPERIENCE:

• 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 of professional experience in technical software development, with a focus on performance engineering, communication libraries, networking, or GPU/distributed systems.

ACADEMIC CREDENTIALS:

• 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.

LOCATION:

Austin, Texas or San Jose, California.

This role is not eligible for visa sponsorship.

#LI-MV1

Benefits offered are described: AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.

This posting is for an existing vacancy.