AI Platform Architect

You will design and oversee the comprehensive infrastructure stack that powers the most demanding distributed AI workloads. Moving beyond individual hardware components, you will act as the unifying technical authority across hardware, software, compute, network, and storage. You will architect 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 AI research and deployment teams have a flawless, frictionless, and extraordinarily powerful platform at their disposal.

Responsibilities

• 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
• Influence the strategy for AI workload scheduling and orchestration, using tools like Kubernetes or Slurm to manage distributed training jobs, model check-pointing, and inference serving at massive scale
• Profile and eliminate system-level bottlenecks across the entire AI pipeline, tuning deep learning frameworks down to OS-level NUMA pinning and I/O scheduling
• Work closely with software, firmware, and OS engineering to influence platform design so the software stack fully exploits underlying hardware capabilities, including ARM mesh interconnects and advanced merchant silicon features
• Drive the 3-to-5-year technical vision for the AI platform and collaborate with subject matter experts across processor, memory, storage, GPU, thermal, mechanical, BIOS, and manageability disciplines
• Define requirements specifications and present them to internal and external silicon teams to influence features, board routing guidelines, power and thermal targets, and feeds and speeds
• Conduct market competitive analysis including TCO (OPEX/CAPEX) analysis of new technologies

Requirements

• 4+ years functioning as a Lead or Principal Architect for large-scale AI or machine learning platforms in systems engineering, cloud architecture, HPC, or hardware engineering
• Deep practical knowledge of how large models are trained and deployed, including data/tensor/pipeline parallelism and infrastructure requirements of modern LLM architectures
• Authoritative understanding of system-level bottlenecks and data pathways, including PCIe Gen 5/6, NVMe namespaces, and RDMA (RoCEv2/InfiniBand) integration
• Experience with container orchestration platforms and infrastructure-as-code (IaC) tailored for GPU-heavy bare-metal and cloud environments
• Exceptional ability to bridge AI researchers/data scientists and low-level hardware/CPU/memory/storage/GPU/network engineers, translating model requirements into infrastructure specifications
• Ability to generate platform engineering requirement specifications to guide and influence future silicon designs
• Hands-on experience with rack-as-a-system AI platforms integrating latest networking, cooling, and GPU technologies (desirable)
• Working knowledge of scripting languages such as Python/JSON to characterize workloads on bare metal AI compute systems (desirable)

Benefits

• Medical, dental and vision coverage
• Flexible Spending Accounts (FSAs)
• Health Savings Accounts (HSAs)
• Disability and life insurance
• 401(k) retirement plan
• Commuter benefits
• Wellness services
• Employee Assistance Programme (EAP)
• Flexible working