Principal Distributed Systems Software Engineer, Collectives
AMD · San Jose
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THE ROLE:
As a core member of the Collectives and Network Performance team, you will play a pivotal role in defining and delivering industry-leading AI pre-training and distributed inference performance on AMD GPUs. Your expertise will be critical in optimizing collective communication libraries, scale-up and scale-out networking software, and training/inference performance across multi-GPU and multi-node systems. You will engage with internal hardware, GPU library, compiler, runtime and ROCm teams as well as open-source maintainers to ensure seamless integration of optimizations, applying advanced engineering principles to drive continuous improvement across multiple generations of AMD GPUs and a wide range of state-of-the-art AI models.
THE PERSON:
Seeking an industry-leading expert in collective communication and network performance, with advanced technical and analytical skills in Linux environments and deep C++ expertise. The ideal candidate will excel in providing technical leadership, guiding teams, and driving projects and initiatives independently. You will define goals, scope, and own development efforts while collaborating effectively within a high-performing team. You have deep knowledge of network, NIC and GPU hardware architecture, hands-on experience mapping model architectures to low-level software and hardware, and a strong grasp of current trends in distributed training and inference at scale.
KEY RESPONSIBILITIES:
- Drive Collectives & Network Strategy: Define goals, scope and roadmap for a major area of AMD’s collectives and network optimizations, and align them with hardware, framework and ROCm roadmaps.
- Architect Collective Communication Libraries: Lead the design and development of collective algorithms and optimizations (e.g., all-reduce, all-to-all, all-gather) across scale-up and scale-out networks.
- Optimize Distributed Training & Inference: Lead performance tuning, profiling and analysis of large-scale LLM, diffusion, multimodal, RecSys and generative AI models on single-node and multi-node systems, and drive the tradeoffs and design decisions.
- Hardware-Software Co-Design: Participate in co-design of future scale-up networks, NICs and scale-out networks, and represent software performance requirements to hardware teams.
- Performance Modeling & Tooling: Drive frameworks, tools and infrastructure for performance estimation, modeling, regression tracking and reporting.
- Collaborate with Framework & Library Teams: Work closely with internal GPU library, compiler and runtime teams, and with open-source maintainers (e.g., PyTorch, JAX, vLLM, SGLang), to ensure optimizations are aligned with requirements and integrated upstream.
- Customer & Partner Guidance: Provide guidelines on efficient network load balancing, workload scheduling and model sharding strategies.
- Communicate with Leadership: Present performance analysis and modeling results to stakeholders and senior leadership, with concrete recommendations.
- Software Engineering Best Practices: Apply sound engineering principles and set standards to ensure robust, maintainable solutions.
- Lead, Guide & Mentor: Provide strategic direction and mentorship to junior team members, fostering growth and collaboration through code reviews, knowledge sharing and technical guidance.
PREFERRED EXPERIENCE:
- Collective Communication & Network Expertise: Deep expertise in designing and optimizing collective communication libraries (e.g., RCCL, NCCL, MPI) and a thorough understanding of NIC, RDMA, scale-up and scale-out network architectures and their impact on model performance.
- Performance Analysis & Modeling: Strong technical expertise and proven experience in performance analysis, projection and bottleneck identification across the full stack, from model to hardware.
- Deep Learning Integration: Proven ability to integrate optimized communication into ML frameworks such as PyTorch, JAX, vLLM and SGLang, with a focus on throughput, scalability and efficient execution for training and inference.
- Software Engineering Excellence: Advanced proficiency in C++ and Python with deep experience in performance tuning, debugging and robust test design, ensuring reliable, maintainable, high-performance codebases.
- High-Performance Computing: Broad and in-depth experience with large-scale, heterogeneous compute environments; adept at optimizing AI workloads for performance, efficiency and resource utilization across clusters.
- Technical Leadership & Communication: Demonstrated ability to lead initiatives across teams, mentor and coach engineers, and communicate effectively in writing and presentations, both internally and externally.
- Professional experience: Senior level of industry experience in technical software development, with a focus on performance engineering, communication libraries, networking, and GPU/distributed systems.
ACADEMIC CREDENTIALS:
- Master’s and/or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
LOCATION:
San Jose, CA or Austin, TX. Onsite.
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 a core member of the Collectives and Network Performance team, you will play a pivotal role in defining and delivering industry-leading AI pre-training and distributed inference performance on AMD GPUs. Your expertise will be critical in optimizing collective communication libraries, scale-up and scale-out networking software, and training/inference performance across multi-GPU and multi-node systems. You will engage with internal hardware, GPU library, compiler, runtime and ROCm teams as well as open-source maintainers to ensure seamless integration of optimizations, applying advanced engineering principles to drive continuous improvement across multiple generations of AMD GPUs and a wide range of state-of-the-art AI models.
THE PERSON:
Seeking an industry-leading expert in collective communication and network performance, with advanced technical and analytical skills in Linux environments and deep C++ expertise. The ideal candidate will excel in providing technical leadership, guiding teams, and driving projects and initiatives independently. You will define goals, scope, and own development efforts while collaborating effectively within a high-performing team. You have deep knowledge of network, NIC and GPU hardware architecture, hands-on experience mapping model architectures to low-level software and hardware, and a strong grasp of current trends in distributed training and inference at scale.
KEY RESPONSIBILITIES:
- Drive Collectives & Network Strategy: Define goals, scope and roadmap for a major area of AMD’s collectives and network optimizations, and align them with hardware, framework and ROCm roadmaps.
- Architect Collective Communication Libraries: Lead the design and development of collective algorithms and optimizations (e.g., all-reduce, all-to-all, all-gather) across scale-up and scale-out networks.
- Optimize Distributed Training & Inference: Lead performance tuning, profiling and analysis of large-scale LLM, diffusion, multimodal, RecSys and generative AI models on single-node and multi-node systems, and drive the tradeoffs and design decisions.
- Hardware-Software Co-Design: Participate in co-design of future scale-up networks, NICs and scale-out networks, and represent software performance requirements to hardware teams.
- Performance Modeling & Tooling: Drive frameworks, tools and infrastructure for performance estimation, modeling, regression tracking and reporting.
- Collaborate with Framework & Library Teams: Work closely with internal GPU library, compiler and runtime teams, and with open-source maintainers (e.g., PyTorch, JAX, vLLM, SGLang), to ensure optimizations are aligned with requirements and integrated upstream.
- Customer & Partner Guidance: Provide guidelines on efficient network load balancing, workload scheduling and model sharding strategies.
- Communicate with Leadership: Present performance analysis and modeling results to stakeholders and senior leadership, with concrete recommendations.
- Software Engineering Best Practices: Apply sound engineering principles and set standards to ensure robust, maintainable solutions.
- Lead, Guide & Mentor: Provide strategic direction and mentorship to junior team members, fostering growth and collaboration through code reviews, knowledge sharing and technical guidance.
PREFERRED EXPERIENCE:
- Collective Communication & Network Expertise: Deep expertise in designing and optimizing collective communication libraries (e.g., RCCL, NCCL, MPI) and a thorough understanding of NIC, RDMA, scale-up and scale-out network architectures and their impact on model performance.
- Performance Analysis & Modeling: Strong technical expertise and proven experience in performance analysis, projection and bottleneck identification across the full stack, from model to hardware.
- Deep Learning Integration: Proven ability to integrate optimized communication into ML frameworks such as PyTorch, JAX, vLLM and SGLang, with a focus on throughput, scalability and efficient execution for training and inference.
- Software Engineering Excellence: Advanced proficiency in C++ and Python with deep experience in performance tuning, debugging and robust test design, ensuring reliable, maintainable, high-performance codebases.
- High-Performance Computing: Broad and in-depth experience with large-scale, heterogeneous compute environments; adept at optimizing AI workloads for performance, efficiency and resource utilization across clusters.
- Technical Leadership & Communication: Demonstrated ability to lead initiatives across teams, mentor and coach engineers, and communicate effectively in writing and presentations, both internally and externally.
- Professional experience: Senior level of industry experience in technical software development, with a focus on performance engineering, communication libraries, networking, and GPU/distributed systems.
ACADEMIC CREDENTIALS:
- Master’s and/or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
LOCATION:
San Jose, CA or Austin, TX. Onsite.
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.