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Salary
$197k – $340k per year (Estimated)
Location
In office (San Francisco)
Seniority
Staff
Employment
Full-Time
Overview
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Hyperbolic runs an open access cloud that aggregates idle GPUs into affordable capacity for artificial intelligence training and inference. Founded in 2023 by researchers from Berkeley and the University of Washington, it offers both raw compute rental and hosted open model endpoints. The company aims to keep frontier-scale experimentation available outside the large hyperscalers.

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're seeking a very 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

Preferred Qualifications

  • Familiarity with high-performance networking technologies such as InfiniBand and RoCE (RDMA over Converged Ethernet)

  • Experience with distributed storage systems such as Ceph, Weka, or VAST Data

Hyperbolic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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