Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. By aggregating 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.
As we prepare for growth after our Series A, our team — led by co-founders with PhDs in AI, Math, and Computer Science — is poised to redefine computing.
About the Role
We're seeking a Senior Infrastructure Engineer to help build and scale Hyperbolic's GPU Cloud Marketplace, by building a multi-tenancy provisioning and virtualization solution. This is a foundational role where you'll be responsible for transforming raw GPUs from diverse global suppliers into a programmable, orchestrated pool that serves thousands of AI developers and researchers. You'll work at the cutting edge of cloud infrastructure, building the core orchestration layer that enables our platform to deliver up to 75% cost savings compared to traditional cloud providers.
Who You Are
Deep understanding of bare-metal provisioning and lifecycle management, including IPMI/Redfish, BMC-based remote management, PXE boot, and automated OS deployment workflows
Deep understanding of GPU scheduling and orchestration, including GPU type awareness, memory management, topology considerations, placement strategies for multi-GPU jobs, and fragmentation minimization
Strong infrastructure and DevOps engineering skills with proficiency in Terraform or Pulumi, CI/CD for infrastructure, secrets management, configuration management, and observability stack implementation
Experience with storage and data infrastructure for AI/ML workloads, including object storage, high-IOPS block storage, and distributed file systems for training data and checkpoints
Proficiency with API design and cloud-init for automated provisioning and configuration
Solid understanding of GPU architecture, CUDA, and GPU compute optimization
Highly collaborative team player with excellent communication skills across technical and non-technical stakeholders
Proven ability to work effectively with hardware vendors and vendor engineering teams to troubleshoot issues and optimize integrations
Experience building and scaling cloud infrastructure or distributed systems in production environments