AI Platform Architect at Graphcore in Austin, TX

About us

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute.

It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation.

Job Summary

We are seeking for a visionary AI Platform Architect to design and oversee the comprehensive infrastructure stack that powers our most demanding distributed AI workloads. Moving beyond individual hardware components, this role acts as the unifying technical authority across hardware, software, compute, network, and storage. You will be responsible for architecting a cohesive, AI rack scale platform optimized for

trillion-parameter LLM training and high-throughput inference. By orchestrating everything from advanced clustering and distributed training frameworks down to the physical layer—spanning PCIe Gen 5/6 pathways, NVMe storage topologies, and RDMA fabrics—you will ensure our AI research and deployment teams have a flawless, frictionless, and extraordinarily powerful platform at their disposal.

Responsibilities and Duties

• End-to-End Platform Architecture: Define the holistic architecture for highly clustered AI environments, ensuring zero-bottleneck data flow between parallel storage systems, AI compute nodes, and ultra-high-bandwidth network fabrics.
• Workload Orchestration: Influence the strategy for AI workload scheduling and orchestration, utilizing tools like Kubernetes or Slurm to manage distributed training jobs, model check-pointing, and inference serving at massive scale.
• Full-Stack Optimization: Profile and eliminate system-level bottlenecks across the entire AI pipeline, tuning everything from deep learning frameworks (PyTorch, DeepSpeed, etc.) down to OS-level NUMA pinning and I/O scheduling.
• Hardware-Software Co-design: Work closely with software, firmware, and OS engineering to influence platform design, ensuring the software stack fully exploits underlying hardware capabilities, including complex ARM mesh interconnects (RNI, HNF, SNF) and advanced merchant silicon features.
• Silicon Influencing Strategy: Drive the 3-to-5-year technical vision for the AI platform. Collaborate closely with subject matter experts in processor, memory, storage, GPU, thermal, mechanical, BIOS, and Manageability disciplines to define requirements specifications to communicate and present to internal and external silicon teams to influence features, optimized board routing guidelines, power and thermal targets, and the correct feeds and speeds for a competitive AI platform. This will require a deep knowledge of the AI industry and significant market competitive analysis including TCO (OPEX / CAPEX) analysis of new technologies.

Candidate Profile

Essential:

• Experience: Demonstrated ability in systems engineering, cloud architecture, or HPC, hardware engineering with at least 4+ years functioning as a Lead or Principal Architect for large-scale AI or machine learning platforms.
• Distributed AI Frameworks: Deep practical knowledge of how large models are trained and deployed, including data/tensor/pipeline parallelism and the infrastructure requirements of modern LLM architectures.
• Systems Interconnects: Authoritative understanding of system-level bottlenecks and data pathways, including deep familiarity with PCIe Gen 5/6, NVMe namespaces, and RDMA (RoCEv2/InfiniBand) integration.
• Orchestration & Containerization: Experience with container orchestration platforms and infrastructure-as-code (IaC) tailored for GPU-heavy bare-metal and cloud environments.
• Cross-Domain Leadership: Exceptional ability to bridge the gap between AI researchers/data scientists and low-level hardware/CPU/memory/storage/GPU/network engineers, translating model requirements into strict infrastructure specifications. Ability to generate Platform engineering requirement specifications that can be used to guide and influence future silicon designs.

Desirable

• Rack scale GPU AI Platforms experience: Hands on experience with rack-as-a-system
• AI platforms that integrate all the latest networking, cooling, and GPU technologies currently present in the market.
• Software / Scripting experience: Working knowledge of scripting language such as Python/JSON to characterize workloads on bare metal AI compute systems to expose issues with current Neural engine silicon solutions.