8 days ago
San Jose, CA, USAStaff+
Responsibilities
- Lead the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters.
- Architect CPU/NUMA and GPU topology, accelerator interconnects, networking, storage, identity, hardware and software baselines, and RDMA-capable fabrics.
- Design resource governance and workload placement using bare metal, virtual machines, Slurm, and Kubernetes.
- Standardize provisioning, configuration, fleet upgrades, images, containers, automation, infrastructure as code, and CI/CD.
- Define production acceptance criteria, service-level objectives, observability, fault recovery, incident analysis, and capacity planning.
- Benchmark HPC and distributed AI workloads and improve scaling efficiency, throughput, inference latency, GPU utilization, reliability, and cost.
- Incorporate federated identity, least privilege, tenant and network isolation, secrets management, container controls, audit logging, and vulnerability management.
- Lead architecture reviews, proofs of concept, provider evaluations, technical problem solving, documentation, mentoring, and stakeholder alignment.
Requirements
- Significant experience in infrastructure, cloud engineering, or distributed systems, including production HPC or GPU platform design and delivery; equivalent practical experience is accepted.
- Experience leading large-scale cluster deployments involving hundreds of nodes, substantial multi-GPU environments, or comparable technical complexity.
- Experience with a major cloud platform and integration between cloud and on-premises systems; specialized GPU provider experience is beneficial.
- Strong Linux troubleshooting experience across CPU/NUMA topology, PCIe, GPU connectivity, drivers, virtualization, containers, and distributed runtimes.
- Hands-on Slurm scheduling and resource-governance experience plus Kubernetes or comparable orchestration experience.
- Understanding of MPI, distributed training, collective communication, AI inference infrastructure, HPC networking, storage, parallel I/O, checkpointing, and enterprise connectivity.
- Experience with GPU computing and distributed AI workloads, especially AMD Instinct GPUs, ROCm, RCCL, and multi-node PyTorch workloads.
- Experience with parallel and shared storage platforms, infrastructure as code, configuration management, scripting, Git-based CI/CD, observability, and SRE practices.
- Ability to lead cross-functional technical work, communicate architecture decisions and tradeoffs, and guide projects through production acceptance.
- Experience coordinating rack power, liquid cooling, facility capacity, hardware, and data center constraints affecting GPU platform design.
- Experience with LLM serving platforms such as vLLM or SGLang, model parallelism, inference capacity planning, and usage accounting is preferred.
- Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
Benefits
- AMD benefits are offered, with details provided through AMD’s benefits overview.
- The role is not eligible for visa sponsorship.
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.