HPC & AI Infrastructure Engineer

Advanced Micro Devices, Inc•San Jose, CA

About The Position

AMD IT’s Compute Platforms Engineering team is seeking an HPC & AI Infrastructure Engineer to design, deploy, automate, and operate CPU and GPU infrastructure supporting high-performance computing, distributed AI training, and inference workloads across on-premises and cloud environments. The role delivers infrastructure engineering projects, troubleshoots complex systems issues, and improves platform performance, scalability, and reliability. The engineer collaborates with architects, operations teams, researchers, network engineers, cloud partners, and technology vendors to bring new capabilities into production. The position suits someone who enjoys complex systems problem-solving, uses data and systematic analysis to troubleshoot, and looks for ways to simplify operations through automation. The role requires clear technical communication, effective independent and collaborative work, and willingness to seek input when decisions affect broader architecture or other teams. A bachelor’s or master’s degree in computer science, computer engineering, or a related field—or an equivalent combination of education and relevant professional experience—is required. This role is not eligible for visa sponsorship. AMD benefits are described in its “AMD benefits at a glance” materials.

Requirements

Bachelor’s or master’s degree in computer science, computer engineering, or a related field, or an equivalent combination of education and relevant professional experience.
Ability to troubleshoot complex technical issues, organize competing priorities, communicate technical information clearly, and collaborate across engineering and operations teams.

Nice To Haves

Experience supporting Linux systems, HPC infrastructure, cloud platforms, GPU infrastructure, or distributed computing environments.
Experience deploying, configuring, supporting, or operating HPC or GPU clusters.
Linux administration and troubleshooting experience across operating systems, networking, storage, and system performance.
Experience with Slurm, Kubernetes, or comparable workload scheduling and orchestration platforms.
Experience supporting GPU drivers, containerized GPU workloads, or accelerator software stacks; familiarity with AMD Instinct GPUs, ROCm, RCCL, or comparable technologies is beneficial.
Familiarity with distributed computing concepts such as MPI, collective communication, and multi-node training.
Experience working with high-speed Ethernet, InfiniBand, or comparable cluster networking technologies.
Familiarity with RDMA and RoCEv2 concepts, including queue pairs, priority flow control (PFC), explicit congestion notification (ECN), and their relationship to fabric performance.
Familiarity with leaf-spine topologies, fabric oversubscription, ECMP, and the impact of network latency, bandwidth, and congestion on distributed workloads.
Experience troubleshooting or supporting high-speed cluster interconnects such as RoCEv2 or InfiniBand.
Familiarity with Kubernetes networking technologies for GPU workloads, including CNI plugins, SR-IOV, or RDMA device plugins, is beneficial.
Experience with shared or parallel storage technologies such as NFS, Lustre, BeeGFS, IBM Storage Scale, Weka, or comparable technologies.
Experience using Python, Bash, or comparable scripting languages for infrastructure automation.
Familiarity with configuration management and infrastructure-as-code technologies such as Ansible, SaltStack, Terraform, or comparable tools.
Experience with Git-based workflows and monitoring or observability technologies such as Prometheus, Grafana, or comparable platforms.
Experience with cloud infrastructure such as OCI, Microsoft Azure, AWS, specialized GPU cloud providers, or comparable environments.
Familiarity with virtualization technologies such as KVM is beneficial.
Familiarity with AI frameworks and inference technologies such as PyTorch, vLLM, SGLang, or comparable technologies is beneficial.

Responsibilities

Deploy, configure, and maintain Linux-based CPU and GPU clusters in accordance with architecture and security standards.
Administer Slurm and Kubernetes environments, including scheduling, resource allocation, access controls, and workload troubleshooting.
Support distributed AI training and inference platforms by managing software environments, containers, and workload deployments.
Automate infrastructure provisioning, configuration, and lifecycle management using infrastructure-as-code, configuration management, and scripting.
Validate hardware, BIOS, firmware, GPU drivers, and runtime compatibility; support upgrades with testing, rollback planning, and user communication.
Configure and troubleshoot host networking for HPC and GPU workloads, including high-speed NICs, RDMA interfaces, IP addressing, VLANs, MTU settings, and routing.
Partner with network engineering teams to deploy and validate RoCEv2 or InfiniBand fabrics, including connectivity, congestion management, and end-to-end RDMA performance.
Troubleshoot issues across compute, GPU, networking, storage, virtualization, and container environments.
Conduct cluster acceptance testing and workload benchmarking to identify bottlenecks and improve performance, utilization, and reliability.
Implement monitoring, dashboards, and alerts; contribute to incident resolution, root-cause analysis, and preventive improvements.
Apply security updates and support vulnerability remediation in partnership with security and operations teams.
Create and maintain technical documentation, operating procedures, and deployment runbooks.
Collaborate with infrastructure partners, cloud providers, and hardware vendors to deliver projects and resolve technical issues.