VP of Engineering (AI)

Hyphen Hyphen LLC

San Francisco, CA, United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.juju.com

Tech stack

Artificial Intelligence Cloud Computing Computer Clusters Continuous Integration Software Debugging Linux Distributed Systems Reliability Engineering Cloud Platform System Kubernetes Storage Technologies

Job description

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

Requirements

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