VP of Engineering
- Level
- Lead
- Work type
- Hybrid · San Francisco
Not stated in the posting: salary, visa.
Open. First seen 2 October 2026.
About the role
Hyperbolic Labs's standard text
Who We Are
Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. By making better use of idle computing resources across the globe, we offer an innovative GPU marketplace and AI inference service that promise affordability and accessibility for all. As pioneers at the intersection of AI and open-source technology, we believe in an open future where AI innovation is limited only by imagination, not by access to resources.
We're looking for forward-thinking individuals who share our passion for making AI universally accessible, secure, and affordable. Join us in building a platform that empowers innovators everywhere to turn their visionary AI projects into reality.
About the Role
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.
What You'll Own
Cloud Infrastructure Architecture
- 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
GPU Cloud Platform
- 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
Technical Leadership
- 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
Infrastructure & Reliability
- 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
Organizational Leadership
- 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
Required Experience
Must-Have Background
- 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
Deep Technical Expertise
- 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
Strongly Preferred
- 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
Hyperbolic Labs's standard text
Hyperbolic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
What this role asks for
77% of this description is written for the role. The rest is Hyperbolic Labs's standard text, folded away below.
Read against Hyperbolic Labs's other open postings on this site.
The team: Engineering HIRES STEADILY
This role is one of 8 open in Engineering. 1 was posted in the last 30 days and the oldest in February: a team that hires all year rather than one being built.
WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS
Kubernetes (5 of 8), CI/CD (4 of 8), Linux (3 of 8), AWS (2 of 8), CUDA (2 of 8), PostgreSQL (2 of 8), Python (2 of 8)
OTHER ROLES ON THIS TEAM
- Senior Data Engineer posted Oct 5
- Member of Technical Staff - Inference posted Sep 9
- Technical Support Engineer posted Aug 28
- Forward Deployed Infrastructure Engineer - Eastern US posted Apr 16
- Member of Technical Staff - Platform posted Mar 31
- Member of Technical Staff - GPU Infrastructure posted Mar 26
- Member of Technical Staff - Full Stack posted Feb 10
From the team and dates on Hyperbolic Labs's own job board.
Salary context
11 other open postings titled VP of Engineering state a salary: median USD 250,000 to USD 310,000 a year.
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