VP of Engineering

Lead the foundational infrastructure powering Hyperbolic's AI cloud platform, including GPU orchestration, compute scheduling, networking, storage, distributed systems, Kubernetes, observability, CI/CD, security, SRE, and platform engineering.

Responsibilities

• Lead the design and evolution of a scalable AI cloud platform.
• Define architecture for GPU orchestration, compute scheduling, networking, storage, and distributed systems.
• Make critical decisions on cloud infrastructure, bare-metal deployments, and platform scalability.
• Build and scale large GPU clusters supporting customer workloads.
• Design GPU provisioning, scheduling, utilization optimization, and capacity management systems.
• Drive platform reliability and performance for AI training and inference workloads.
• Establish best practices for Kubernetes, observability, CI/CD, security, and operational excellence.
• Build SRE and Platform Engineering functions from the ground up.
• Define reliability standards including SLOs, SLIs, incident response, and capacity planning.
• Drive infrastructure automation.
• Recruit and develop Infrastructure, Platform, and SRE teams.
• Partner with executive leadership on company strategy, infrastructure investments, budgets, vendors, and capacity planning.

Requirements

• 12+ years building and operating large-scale infrastructure systems.
• Experience leading infrastructure organizations while remaining hands-on technically.
• Experience building or operating a cloud platform at scale.
• Experience building GPU infrastructure or AI/ML compute platforms.
• Track record scaling infrastructure in high-growth startup environments.
• Expert-level Kubernetes knowledge.
• Experience designing and operating multi-region cloud infrastructure.
• Strong understanding of Linux, networking, distributed systems, and storage architecture.
• Experience with Infrastructure-as-Code and automation frameworks.
• Deep expertise in observability, monitoring, and reliability engineering.
• Experience building highly available production systems.
• Experience with GPU scheduling, Slurm, Kubernetes GPU operators, Ray, or distributed training systems.
• Experience managing thousands of GPUs in production environments.
• Background supporting AI training and inference platforms.