Builds and scales massive Kubernetes clusters for OpenAI's frontier supercomputers, automates bare-metal provisioning, and ensures reliability across data centers for AI model training. Requires expertise in distributed systems, Kubernetes operations, and infrastructure automation.
230k – 490k/yr
On-siteDevOps / SRE
About the role
Responsibilities
Spin up and scale large Kubernetes clusters, including automation for provisioning, bootstrapping, and cluster lifecycle management
Build software abstractions that unify multiple clusters and present a seamless interface to training workloads
Own node bring-up from bare metal through firmware upgrades, ensuring fast, repeatable deployment at massive scale
Improve operational metrics such as reducing cluster restart times (e.g., from hours to minutes) and accelerating firmware or OS upgrade cycles
Integrate networking and hardware health systems to deliver end-to-end reliability across servers, switches, and data center infrastructure
Develop monitoring and observability systems to detect issues early and keep clusters stable under extreme load
Requirements
Deep experience operating or scaling Kubernetes clusters or similar container orchestration systems in high-growth or hyperscale environments
Strong programming or scripting skills (Python, Go, or similar) and familiarity with Infrastructure-as-Code tools such as Terraform or CloudFormation
Comfortable with bare-metal Linux environments, GPU hardware, and large-scale networking
Experience as an infrastructure, systems, or distributed systems engineer in large-scale or high-availability environments
Strong knowledge of Kubernetes internals, cluster scaling patterns, and containerized workloads
Proficiency in cloud infrastructure concepts (compute, networking, storage, security) and in automating cluster or data center operations
Nice-to-Haves
Background with GPU workloads, firmware management, or high-performance computing
Skills
KubernetesPythonGoTerraformLinuxGPUDistributed SystemsInfrastructure As CodeBare MetalNetworking
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