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Member of Technical Staff (GPU Infrastructure)

Exclusive opportunity

Onsite

Member of Technical Staff (GPU Infrastructure)

Hyperbolic

Member of Technical Staff (GPU Infrastructure)

Skills

Machine LearningAITerraform

2 months ago

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Important information


Contract type:

Permanent

Location:

San Francisco, CA, USA

Work mode:

Onsite

Published on:

29 August 2026

What they need


  • 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- 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
  • Strong infrastructure and DevOps engineering skills with proficiency in Terraform or Pulumi, CI/CD for infrastructure, secrets management, configuration management, and observability stack implementation
  • 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
  • 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
  • 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

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