VP of Engineering
Skills
About the Role
You will lead the cloud infrastructure architecture and define the architecture for GPU orchestration compute scheduling networking storage and distributed systems. You will build and scale large GPU clusters supporting customer workloads. You will drive reliability and performance for AI training and inference workloads. You will establish best practices for Kubernetes observability CI CD security and operational excellence. You will build SRE and Platform Engineering functions from the ground up and recruit and develop infrastructure, platform, and SRE capabilities. You will partner with stakeholders on strategy and investments for scalable AI infrastructure.
Requirements
- 12+ years building and operating large-scale infrastructure systems
- Experience leading infrastructure organizations while remaining hands-on technically
- Experience building GPU infrastructure or AI ML compute platforms
- Proven 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
Responsibilities
- Lead the design and evolution of a scalable AI cloud platform
- Define the architecture for GPU orchestration compute scheduling networking storage and distributed systems
- Make critical decisions regarding cloud infrastructure bare metal deployments and platform scalability
- Personally participate in architecture reviews and key technical initiatives
- Build and scale large GPU clusters supporting customer workloads
- Design systems for GPU provisioning scheduling utilization optimization and capacity management
- Drive platform reliability and performance for AI training and inference workloads
- Partner with stakeholders on infrastructure requirements for next generation AI systems
- 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 processes and capacity planning
- Drive automation across infrastructure operations
- Recruit and develop infrastructure platform and SRE capabilities