VP of Engineering

Requirements

• 12+ years building and operating large-scale infrastructure systems
• Experience leading infrastructure organizations while remaining hands‑on technically
• Previous experience building or operating a cloud platform at scale
• 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
• (Desirable) Experience with GPU scheduling, Slurm, Kubernetes GPU operators, Ray, or distributed training systems
• (Desirable) Experience managing thousands of GPUs in production environments
• (Desirable) Background supporting AI training and inference platforms

What the job involves

• We are seeking a highly technical Vice President of Infrastructure to build and scale the foundational infrastructure powering our AI cloud platform
• This is a hands‑on executive leadership role
• While you will own infrastructure strategy, organizational growth, and executive‑level decision making, we expect you to remain deeply engaged in architecture, design, and engineering execution
• You should expect to spend approximately 30-40% of your time directly contributing to technical design, architecture reviews, debugging critical production issues, and partnering with engineers on implementation
• The ideal candidate has previously built and scaled cloud platforms, preferably GPU‑native cloud infrastructure supporting AI training and inference workloads
• You have experience operating at the intersection of executive leadership and hands‑on engineering and are excited to help build both the technology and the team
• Lead the design and evolution of our 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 closely with engineering teams on infrastructure requirements for next generation AI systems
• Remain deeply involved in engineering decisions and technical direction
• Contribute directly to infrastructure design and implementation efforts
• Review architecture proposals, system designs, and major infrastructure changes
• Act as the technical escalation point for complex infrastructure challenges
• 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 world‑class Infrastructure, Platform, and SRE teams
• Build a high‑performance engineering culture focused on ownership and execution
• Partner with executive leadership on company strategy and infrastructure investments
• Manage infrastructure budgets, vendor relationships, and capacity planning