VP of Engineering (AI)

Our Client is an AI company working on revolutionizing the AI landscape with our GPU marketplace and AI inference service, making high-performance computing affordable and accessible to all. They are hiring a Executive Engineering hire (SF, Hybrid) to building the Infrastructure, Platform, and SRE functions from the ground up.

Key Responsibilities

• Lead the design and evolution of the AI cloud platform architecture — GPU orchestration, compute scheduling, networking, storage, and distributed systems
• Build and scale large GPU clusters supporting customer workloads, including GPU provisioning, scheduling, utilization optimization, and capacity management
• Personally participate in architecture reviews, system design, and key technical initiatives (expect 40%+ of time on technical contribution)
• Act as the technical escalation point for complex infrastructure challenges — debug production issues, review proposals, and drive decisions
• Establish best practices for Kubernetes, observability, CI/CD, security, and operational excellence
• Build SRE and Platform Engineering functions from scratch — define SLOs, SLIs, incident response, and capacity planning
• Recruit and develop world-class Infrastructure, Platform, and SRE teams
• Partner with executive leadership on company strategy and infrastructure investments
• Manage infrastructure budgets, vendor relationships, and capacity planning

• 12+ years building and operating large-scale infrastructure systems, with experience leading infrastructure organizations while remaining deeply hands-on technically.
• Previous experience building or operating a cloud platform at scale — ideally GPU-native cloud infrastructure supporting AI training and inference workloads
• Expert-level Kubernetes knowledge and experience designing multi-region cloud infrastructure
• Deep expertise in Linux, networking, distributed systems, and storage architecture
• Proven track record scaling infrastructure in high-growth startup environments — not just maintaining systems at large companies
• Strong understanding of Infrastructure-as-Code, automation frameworks, observability, monitoring, and reliability engineering
• Experience building highly available production systems with clear SLOs and incident response processes