System Design Engineer – AI Cluster Storage Architect
About The Position
This is a hands-on role for engineers who thrive on exploration, love solving complex systems problems, and are passionate about HPC and AI. You’ll bring your HPC expertise to a research and development-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. The primary focus for this role is on storage solutions for AI/HPC, benchmarking proof points, and creation of reference architecture and other supporting collateral related specifically to storage solutions in support of AMD-based AI and HPC clustered systems at scale. 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, presentations, and “how it works” guides to help others skill up from an HPC perspective in the AI space. 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!
Requirements
- Engineering mindset: Evidence of end-to-end systems thinking, debugging, and tradeoff decisions
- Storage/data: parallel filesystems (Lustre, BeeGFS), object stores, RDMA, data pipeline throughput and caching strategies
- Extensive knowledge of the current storage vendor landscape
- AI/HPC cluster background: hands-on familiarity with 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
- Enterprise storage solutions (NAS, NFS), particularly large scale, and design patterns for federation/replication, backup, and DR
- 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
- Bachelors or Masters degree in electrical, computer, or related engineering field
Responsibilities
- Apply your HPC 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
- 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
- Perform deep technical evaluations of HPC and AI stacks with a specific focus on storage solutions, documenting how they work, where they fit, and the tradeoffs involved between storage technologies and vendors
- Design and execute reproducible experiments and benchmarking harnesses to compare storage technologies and their fit-for-purpose across AI and HPC workloads
- Develop small reference implementations and tools to validate performance hypotheses, analyze system behavior and more
- Present findings through demos, documentation, and internal talks, and create templates and checklists to support repeatable evaluations and cluster designs
Benefits
- AMD benefits at a glance
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What This Job Offers
Job Type
Full-time
Career Level
Senior
