AI Platform Architect

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.

What We Do

At Graphcore we’re building the future of AI compute. We’re a team of semiconductor software and AI experts with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale. As part of the SoftBank Group backed by significant long-term investment we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems in a place where everyone has the opportunity to make an impact on the company our products and the future of artificial intelligence.

Why Work With Us

Our team is at the forefront of the machine intelligence revolution enabling innovators from all industries to build AI-native products to expand human potential. What we do at Graphcore really makes a difference.

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