Systems Design Engineer - AI Cluster Software

Advanced Micro Devices, Inc•Austin, TX

8h•Hybrid

About The Position

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!

Requirements

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.

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.