TensorWave
AI compute
StaffInfrastructureEngineer–KubernetesPlatform
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“Staff Infrastructure Engineer – Kubernetes Platform at TensorWave. Skills: Kubernetes Platform Engineering, Distributed Systems, Cloud Infrastructure. Owning the design, evolution, and operational reliability of our Kubernetes control plane architecture. Design and evolve Kubernetes control plane architecture across regions”
Industry & Context.
Diagnose and resolve control plane instability, API server saturation, scheduling and resource contention issues; Troubleshoot and optimize pod-to-pod networking, north-south traffic flows, CNI behavior; Deep troubleshooting ability across Kubernetes, Container runtime, Networking stack; Lead root cause analysis for production incidents
Participate in on-call rotation
What They're Looking For.
Must Have
7+ years of experience in infrastructure, platform engineering, or distributed systems, Deep experience operating Kubernetes at scale in production environments, Linux systems expertise, Deep troubleshooting ability across Kubernetes, Container runtime, Networking stack
Nice to Have
Experience in CSP, hyperscale, or equivalent large-scale environments strongly preferred, Experience with virtual cluster technologies (vcluster, Kamaji, or similar), Experience supporting GPU workloads in Kubernetes, Familiarity with NUMA-aware scheduling, Familiarity with Topology-aware workloads, Awareness of RDMA and high-throughput networking environments, Experience with observability platforms (Prometheus, Grafana, etc.)
What You'll Do.
and operational reliability of our Kubernetes control plane architecture
Design and evolve Kubernetes control plane architecture across regions
Define and implement multi-tenant cluster models
Drive transition from standalone clusters to regionally managed platform models
Define standards for isolation boundaries
resource segmentation
Own the reliability and behavior of Kubernetes platforms in production
Participate in on-call rotation and lead incident response
Diagnose and resolve control plane instability
API server saturation
scheduling and resource contention issues
Ensure consistent lifecycle management across clusters (provisioning
Design and implement strategies for regional scaling
multi-data center cluster deployments
Ensure consistent behavior and reliability across environments
Define cluster topology and failure domain strategies
Design ingress and egress architectures at cluster level and regional level
Troubleshoot and optimize pod-to-pod networking
north-south traffic flows
Improve observability across control plane components
cluster health and performance
Define and implement resilience strategies aligned with platform goals
Lead root cause analysis for production incidents
How You'll Work.
Team & Collaboration
working closely with cross-functional partners to support business objectives; Work closely with DevOps engineers (automation and CI/CD) and Infrastructure teams (compute, storage, networking); Align Kubernetes platform design with underlying infrastructure capabilities
Full Job Description
About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure. About the Role We’re looking for a Kubernetes Platform Staff Infrastructure Engineer to join our team during an exciting phase of growth. In this role, you’ll be responsible for owning the design, evolution, and operational reliability of our Kubernetes control plane architecture, working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact. What You’ll Do Platform Architecture & Strategy - Design and evolve Kubernetes control plane architecture across regions - Define and implement multi-tenant cluster models, including shared control planes, virtual cluster approaches (e.g., vcluster, Kamaji) - Drive transition from standalone clusters to regionally managed platform models - Define standards for isolation boundaries, resource segmentation, policy enforcement Platform Ownership & Operations - Own the reliability and behavior of Kubernetes platforms in production - Participate in on-call rotation and lead incident response - Diagnose and resolve control plane instability, API server saturation, scheduling and resource contention issues - Ensure consistent lifecycle management across clusters - provisioning, upgrades, scaling Multi-Region Scaling - Design and implement strategies for regional scaling, multi-data center cluster deployments - Ensure consistent behavior and reliability across environments - Define cluster topology and failure domain strategies Networking & Data Plane Integration - Design ingress and egress architectures at cluster level and regional level - Troubleshoot and optimize pod-to-pod networking, north-south traff
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