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Cirrascale Cloud Services

Public IaaS for AI, multi-GPU

Cloud Engineer

Cloud EngineeringConfirmed live in the last 24 hours
Full-timeSan Diego, CA, USAIn-person

About the Job

Requirements

  • At least 7 years of experience managing and scaling enterprise storage systems.
  • At least 3 years of hands-on production experience with Ceph and/or WEKA.
  • Deep knowledge of object, block, and parallel file system storage architectures.
  • Strong understanding of RDMA, InfiniBand, NVMe over Fabrics, and distributed metadata systems.
  • Proficiency in Linux, preferably Ubuntu or CentOS, and scripting with Bash and Python.
  • Experience tuning performance for AI and machine-learning workloads using storage-intensive frameworks such as TensorFlow and PyTorch.
  • Familiarity with containerized and virtualized environments, including Docker, Kubernetes, and KVM.
  • Strong troubleshooting and diagnostic skills in large-scale, multi-tenant environments.

Responsibilities

  • Architect, deploy, and manage high-performance, scalable storage solutions based on Ceph and WEKA for AI and deep-learning workloads.
  • Optimize IOPS and throughput across distributed systems supporting hundreds of GPUs per cluster.
  • Develop and maintain infrastructure-as-code templates for automated storage deployments.
  • Monitor system performance and implement improvements to ensure low latency, high bandwidth, and data integrity.
  • Lead incident response and root cause analysis for storage-related issues across production environments.
  • Collaborate with system engineers, network teams, and customer success to tailor storage performance to specific workload needs.
  • Evaluate and integrate new storage technologies and NVMe architectures into the AI stack.
  • Write and maintain detailed technical documentation and runbooks.
  • Contribute to strategic infrastructure planning and scaling initiatives.

Desired Qualifications

  • Experience with AI model training workflows, particularly storage I/O patterns in multi-node GPU clusters.
  • Familiarity with storage solutions from NetApp, DDN, VAST Data, and cloud-native offerings.
  • Experience with monitoring tools such as Prometheus and Grafana, and configuration management tools such as Ansible and Terraform.
  • Knowledge of data governance and compliance standards in AI environments.

About the Company

Cirrascale Cloud Services

Public IaaS for AI, multi-GPU

Cirrascale Cloud Services provides dedicated AI-focused cloud infrastructure (public and private bare-metal IaaS) with flat-rate monthly pricing and no data transfer fees. Its Cirrascale AI Innovation Cloud gives access to AI accelerators from NVIDIA, Cerebras, AMD, and Qualcomm, enabling development, training, fine-tuning, and deployment on high-performance hardware with fast networking and scalable storage, plus managed services for setup and access control. It differentiates itself by offering specialized multi-GPU/bare-metal cloud with predictable pricing and no data transfer fees for large-scale operators, startups, researchers, and Fortune 500 clients. The goal is to help customers build and run AI models quickly and at scale using ready-to-use infrastructure and optional hands-on management, reducing the need for in-house DevOps and hardware management.

Company size
51-200
Company stage
Early VC
Total funding
$59.1M
Headquarters
San Diego, CA
Founded
2010

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