Systems Design Engineer - AI Cluster Software

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE ROLE:  This is a hands-on role for engineers who thrive on exploration, love solving complex systems problems, and are passionate about AI, HPC, and large-scale infrastructure. You’ll bring your expertise to a software-focused team that investigates AI infrastructure across compute, storage, networking, and orchestration layers. Your work and knowledge will help shape reference architectures, configuration guides, and reproducible experiments that support internal teams, pre-sales engineers, and customers in making informed hardware and software decisions. Our team operates across industry verticals as subject matter experts in the AI stack and across the cluster. We’re building a library of technical artifacts such as design docs, run books, and “how it works” guides to help others inside and outside AMD deploy, manage, and scale AMD-based AI infrastructure. This is a high-autonomy role focused on creation, not operations. If you enjoy building, learning, debugging tough issues, and writing about what you discover, we want to hear from you!   THE PERSON:  You’re an engineer, a systems thinker and professional troubleshooter who sees the big picture and thrives on researching and experimentation. You have hands-on experience with rack- and row-scale performant infrastructure and are eager to explore how AI workloads like inferencing and training fit into large-scale AI infrastructure. You’re not looking for a runbook, you’re looking to build the blueprint. You’re self-directed, proactive, and comfortable navigating ambiguity to solve complex problems. You communicate clearly, enjoy writing technical artifacts that help others understand intricate systems, and collaborate naturally with internal teams and customers. You get excited to teach others what you know. Whether you’re diving into a new stack or refining a reference architecture, you bring curiosity, initiative, and a drive to create.   KEY RESPONSIBILITIES:  Apply your expertise to shape AI infrastructure by creating reference architectures, configuration guides, and deployment blueprints that help internal teams and customers make informed hardware and software decisions. Perform deep technical evaluations of AI stacks across compute, storage, networking, and observability layers, documenting how they work, where they fit, and the tradeoffs involved. Design and execute reproducible experiments and benchmarking harnesses to compare technologies such as schedulers, distributed training libraries, and observability stacks. Develop small reference implementations and tools to validate performance hypotheses, analyze system behavior and more. Build a library of technical artifacts—including presentations, design documents, and “how it works” guides, to support pre-sales engineers and enable others to skill up from an HPC perspective. Present findings through demos, documentation, and internal talks, and create templates and checklists to support repeatable evaluations and cluster designs.   PREFERRED EXPERIENCE:  Engineering mindset: Evidence of end-to-end systems thinking, debugging, and tradeoff decisions. AI/HPC cluster background: hands-on familiarity with at least two schedulers and/or orchestration systems (e.g., Slurm, Kubernetes), MPI/OpenMP, distributed storage patterns, or performance analysis. Comparative analysis: experience writing evaluation docs/RFCs with clear criteria, benchmarks, risks, and recommendations. Strong Linux fundamentals: Linux operating systems, networking, filesystems, containers, performance tooling (perf, flamegraphs, nvprof/rocprof, basic eBPF). Clear communication: ability to turn complex systems into accessible, structured documentation with diagrams and reproducible steps. AMD ecosystem experience: ROCm, RCCL, Instinct GPUs, EPYC platforms, compiler/toolchain impacts, and performance tuning. Distributed training internals: DDP, collective comms, sharded/stateful optimizers; NCCL/RCCL behavior and transport considerations (PCIe, NVLink, IF). Orchestration models: Slurm configuration patterns, Kubernetes for HPC/AI (GPU operators, device plugins), Apptainer/Singularity. Storage/data: parallel filesystems (Lustre, BeeGFS), object stores, RDMA, data pipeline throughput and caching strategies. IaC literacy: Terraform/Ansible for reproducible blueprints—focused on design and sample configs, not running prod clusters. Documentation tooling: reproducible docs/workbooks, literate programming notebooks, CI for benchmarks. ACADEMIC CREDENTIALS:  Bachelors or Masters degree in electrical or computer engineering LOCATIONS:   Austin, Texas Seattle, Washington Santa Clara, California Secaucus, New Jersey Markham, Canada This role is not eligible for visa sponsorship. #LI-CB1 #LI-HYBRID 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.

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:  This is a hands-on role for engineers who thrive on exploration, love solving complex systems problems, and are passionate about AI, HPC, and large-scale infrastructure. You’ll bring your expertise to a software-focused team that investigates AI infrastructure across compute, storage, networking, and orchestration layers. Your work and knowledge will help shape reference architectures, configuration guides, and reproducible experiments that support internal teams, pre-sales engineers, and customers in making informed hardware and software decisions. Our team operates across industry verticals as subject matter experts in the AI stack and across the cluster. We’re building a library of technical artifacts such as design docs, run books, and “how it works” guides to help others inside and outside AMD deploy, manage, and scale AMD-based AI infrastructure. This is a high-autonomy role focused on creation, not operations. If you enjoy building, learning, debugging tough issues, and writing about what you discover, we want to hear from you!   THE PERSON:  You’re an engineer, a systems thinker and professional troubleshooter who sees the big picture and thrives on researching and experimentation. You have hands-on experience with rack- and row-scale performant infrastructure and are eager to explore how AI workloads like inferencing and training fit into large-scale AI infrastructure. You’re not looking for a runbook, you’re looking to build the blueprint. You’re self-directed, proactive, and comfortable navigating ambiguity to solve complex problems. You communicate clearly, enjoy writing technical artifacts that help others understand intricate systems, and collaborate naturally with internal teams and customers. You get excited to teach others what you know. Whether you’re diving into a new stack or refining a reference architecture, you bring curiosity, initiative, and a drive to create.   KEY RESPONSIBILITIES:  Apply your expertise to shape AI infrastructure by creating reference architectures, configuration guides, and deployment blueprints that help internal teams and customers make informed hardware and software decisions. Perform deep technical evaluations of AI stacks across compute, storage, networking, and observability layers, documenting how they work, where they fit, and the tradeoffs involved. Design and execute reproducible experiments and benchmarking harnesses to compare technologies such as schedulers, distributed training libraries, and observability stacks. Develop small reference implementations and tools to validate performance hypotheses, analyze system behavior and more. Build a library of technical artifacts—including presentations, design documents, and “how it works” guides, to support pre-sales engineers and enable others to skill up from an HPC perspective. Present findings through demos, documentation, and internal talks, and create templates and checklists to support repeatable evaluations and cluster designs.   PREFERRED EXPERIENCE:  Engineering mindset: Evidence of end-to-end systems thinking, debugging, and tradeoff decisions. AI/HPC cluster background: hands-on familiarity with at least two schedulers and/or orchestration systems (e.g., Slurm, Kubernetes), MPI/OpenMP, distributed storage patterns, or performance analysis. Comparative analysis: experience writing evaluation docs/RFCs with clear criteria, benchmarks, risks, and recommendations. Strong Linux fundamentals: Linux operating systems, networking, filesystems, containers, performance tooling (perf, flamegraphs, nvprof/rocprof, basic eBPF). Clear communication: ability to turn complex systems into accessible, structured documentation with diagrams and reproducible steps. AMD ecosystem experience: ROCm, RCCL, Instinct GPUs, EPYC platforms, compiler/toolchain impacts, and performance tuning. Distributed training internals: DDP, collective comms, sharded/stateful optimizers; NCCL/RCCL behavior and transport considerations (PCIe, NVLink, IF). Orchestration models: Slurm configuration patterns, Kubernetes for HPC/AI (GPU operators, device plugins), Apptainer/Singularity. Storage/data: parallel filesystems (Lustre, BeeGFS), object stores, RDMA, data pipeline throughput and caching strategies. IaC literacy: Terraform/Ansible for reproducible blueprints—focused on design and sample configs, not running prod clusters. Documentation tooling: reproducible docs/workbooks, literate programming notebooks, CI for benchmarks. ACADEMIC CREDENTIALS:  Bachelors or Masters degree in electrical or computer engineering LOCATIONS:   Austin, Texas Seattle, Washington Santa Clara, California Secaucus, New Jersey Markham, Canada This role is not eligible for visa sponsorship. #LI-CB1 #LI-HYBRID