Member of Technical Staff - AI Cloud Infrastructure

The role

You will architect and build managed cloud services from end to end. This includes designing the platform, control plane, and customer experience for a managed AI cloud.

What you will do

• Architect managed services from 0 to 1.
• Define productization of GPU capacity including isolation, tenant models, and provisioning flows.
• Build control-plane services, customer interfaces, and automated lifecycle systems.
• Onboard and vet infrastructure partners through technical assessments.
• Design end-to-end multi-tenancy across compute, storage, and networking.
• Manage Kubernetes and Slurm environments for large-scale training and inference.
• Lead high-performance storage strategy by deploying parallel storage solutions.
• Define SLOs, observability standards, and incident response protocols.

Required qualifications

• At least 7+ years of experience in infrastructure or platform engineering, including the architecture and launch of a managed cloud or AI platform that reached production users.
• Strong experience with Kubernetes and Slurm and offering them as managed service
• Production experience deploying or operating Lustre or a comparable parallel filesystem such as GPFS, Weka, VAST, or BeeGFS
• A strong grasp of cloud service fundamentals, including control planes, tenancy and isolation models, APIs, quota and metering systems
• Deep Linux systems knowledge
• Mature infrastructure as code practice with tools such as Terraform and Ansible
• Solid programming ability in Python or Go
• Familiarity with GPU infrastructure, including high performance networking with InfiniBand, RoCE, and RDMA, and the GPU software stack

Nice to have

• Prior time at a GPU cloud, a hyperscaler AI service, or an HPC center that delivers compute and storage as a service
• Familiarity with NVIDIA reference architectures such as SuperPOD, along with GPUDirect Storage, NCCL debugging, and DCGM
• Experience with Lustre multitenancy features such as nodemap, fileset mounts, and Kerberos, or with service provider deployments of VAST or Weka
• Experience negotiating with and integrating multiple infrastructure vendors
• Experience running object storage at scale with systems such as S3, Ceph, or MinIO
• Experience building billing, metering, or FinOps pipelines for services that charge by usage

Benefits

• Competitive pay + equity
• Stock options
• Medical
• Dental
• Vision
• 401(k) matching
• Flexible location with 2 WFH days/week

About the company

Emerald AI sits at the intersection of AI and energy, enabling AI data centers to scale without overwhelming the grid. The company is backed by Radical Ventures and NVIDIA.