Head of GPU Cluster Engineering

About Nava

Nava is building next-generation AI infrastructure and inference platforms designed to power the future of AI. We are looking for a Head of GPU Cluster Engineering to lead the architecture, deployment, and operations of large-scale GPU clusters.

This is a highly strategic leadership role responsible for defining the engineering vision, driving execution across multiple infrastructure domains, and ensuring our GPU platforms deliver world-class performance, scalability, and reliability.

What You'll Do

Platform Leadership

• Own end-to-end engineering outcomes, from reference architecture through production operations.
• Define the technical vision, architecture, and operating model for large-scale GPU infrastructure.
• Establish engineering standards, design principles, and operational excellence across the platform.
• Drive platform scalability, resiliency, and performance to support rapidly growing AI workloads.
  
  

Architecture & Technical Strategy

• Lead the design and evolution of GPU cluster architectures across compute, networking, storage, orchestration, and observability.
• Evaluate and drive technology decisions around GPUs, interconnects, storage, Kubernetes, scheduling, and AI infrastructure software.
• Review and approve architecture decisions while balancing performance, reliability, cost, and operational complexity.
• Drive infrastructure standardization and automation across deployments.
  
  

Cross-Functional Leadership

• Lead execution across multiple engineering pillars:
• GPU Compute
• High-Speed Networking
• Storage
• Platform Engineering
• Site Reliability Engineering (SRE)
• Infrastructure Automation
• Partner closely with Product, Supply Chain, Data Centre Operations, and Customer Success teams to ensure successful platform delivery.
• Act as the technical escalation point for critical engineering decisions.
  

Delivery & Operational Excellence

• Own engineering readiness gates from design through production deployment.
• Drive release planning, operational reviews, risk assessments, and post-incident analysis.
• Establish SLAs, SLOs, and operational metrics for platform health and reliability.
• Champion automation, observability, incident management, and continuous improvement across the engineering organization.
  
  

Team Leadership

• Build, mentor, and scale a high-performing engineering organization.
• Develop technical leaders across infrastructure disciplines.
• Foster a culture of engineering excellence, ownership, collaboration, and innovation.
• Support hiring and talent development for critical infrastructure roles.
  
  

What We're Looking For

• 12+ years of experience in infrastructure engineering, distributed systems, cloud platforms, or AI infrastructure.
• Proven experience leading large-scale infrastructure or platform engineering teams.
• Deep understanding of GPU clusters, AI infrastructure, HPC, or large-scale distributed systems.
• Strong expertise across:
• GPU Compute (NVIDIA ecosystem preferred)
• High-performance networking (InfiniBand, RoCE, RDMA)
• Kubernetes and container orchestration
• Distributed storage systems
• Infrastructure automation and observability
• Linux systems and platform engineering
• Experience designing highly available, scalable production infrastructure.
• Strong architectural thinking with the ability to balance technical excellence and business priorities.
• Excellent stakeholder management and cross-functional leadership skills.
  

Nice to Have

• Experience building AI factories, GPU cloud platforms, or large-scale inference infrastructure.
• Familiarity with CUDA, NCCL, Slurm, Ray, Kubeflow, or similar AI infrastructure technologies.
• Experience working with hyperscalers, cloud providers, or AI-first technology companies.
• Exposure to multi-region or global infrastructure deployments.
  
  

Why Join Nava

• Build one of the world's leading AI infrastructure platforms.
• Lead the engineering strategy behind next-generation GPU clusters and AI factories.
• Work alongside world-class engineers solving some of the most challenging infrastructure problems in AI.
• Shape the future of AI infrastructure from architecture through production at global scale.
  
  

Skills: leadership,automation,platforms,gpu,reliability,storage,architecture,cloud,infrastructure,cluster,drive