HPC & AI Infrastructure Engineer

ADVANCE YOUR CAREER. ADVANCE THE WORLD. 

At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future. 

Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.

THE ROLE:

We are seeking an HPC & AI Infrastructure Engineer to join AMD IT’s Compute Platforms Engineering team. You will help 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.

This role involves delivering infrastructure engineering projects, troubleshooting complex systems issues, and improving platform performance, scalability, and reliability. You will collaborate with architects, operations teams, researchers, network engineers, cloud partners, and technology vendors to bring new capabilities into production.

THE PERSON:

You enjoy solving complex systems problems and building reliable infrastructure. You approach troubleshooting using data and systematic analysis and look for opportunities to simplify operations through automation. You can work effectively both independently and collaboratively, communicate technical information clearly, and partner with colleagues across functions, regions, and time zones. You are comfortable seeking input when decisions affect broader platform architecture or other teams.

KEY RESPONSIBILITIES:

• Deploy, configure, and maintain Linux-based CPU and GPU clusters following established 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 and support upgrades through 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 and 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.

PREFERRED EXPERIENCE:

Candidates are not expected to have experience with every technology listed below. Relevant skills may be developed through professional experience, education, training, or other technical pathways.

• 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.
• Ability to troubleshoot complex technical issues, organize competing priorities, communicate technical information clearly, and collaborate across engineering and operations teams.

ACADEMIC CREDENTIALS:

• 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.

This role is not eligible for visa sponsorship.

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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.