Staff Infrastructure Engineer Kubernetes Platform

Staff Infrastructure Engineer - Kubernetes Platform TensorWave Las Vegas, NV Job Details Full-time 16 hours ago Benefits Stock options Health savings account Paid holidays Disability insurance Health insurance Dental insurance 401(k) Flexible spending account Paid time off Parental leave Employee assistance program Vision insurance Qualifications Network troubleshooting Kubernetes Scalable systems Infrastructure architecture design Network architecture APIs Scalability System architecture design Linux Technical troubleshooting support Linux administration 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 build and operate large-scale infrastructure platforms supporting high-performance AI workloads across multiple data centers. Our Kubernetes environments power core platform services and customer-facing workloads, and are evolving toward a managed, multi-tenant, multi-region platform model . We are looking for a Staff Infrastructure Engineer - Kubernetes Platform to own the design, evolution, and operational reliability of our Kubernetes control plane architecture. This role combines architecture and hands-on operational ownership , with a focus on building a scalable, multi-tenant platform comparable in maturity to managed Kubernetes offerings such as GKE or AKS. This is not a cluster administration role. You will be responsible for how Kubernetes operates as a platform across regions. 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 traffic flows, CNI behavior (Cilium preferred) Collaborate with network engineering on high-performance networking integration Observability & Reliability 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 Cross-Team Collaboration Work closely with DevOps engineers (automation and CI/CD) and Infrastructure teams (compute, storage, networking) Align Kubernetes platform design with underlying infrastructure capabilities Who You Are 7+ years of experience in infrastructure, platform engineering, or distributed systems Deep experience operating Kubernetes at scale in production environments Experience in CSP, hyperscale, or equivalent large-scale environments strongly preferred Proven experience scaling Kubernetes across: Multiple clusters Multiple regions or data centers Strong understanding of Kubernetes internals: API server Scheduler Controller manager etcd Experience designing or evolving: Control plane architectures Multi-tenant cluster models Technical Depth Strong Linux systems expertise Deep troubleshooting ability across: Kubernetes Container runtime Networking stack Experience with CNI plugins (Cilium preferred) Strong understanding of: Networking and traffic patterns Resource isolation and scheduling Preferred Experience Experience with virtual cluster technologies (vcluster, Kamaji, or similar) Experience supporting GPU workloads in Kubernetes Familiarity with: NUMA-aware scheduling Topology-aware workloads Awareness of RDMA and high-throughput networking environments Experience with observability platforms (Prometheus, Grafana, etc.) What We Offer Stock Options 100% paid Medical, Dental, and Vision insurance for Employees Company Health Savings Account Contributions 100% paid Short Term and Long Term Disability Insurance for Employees Life and Voluntary Supplemental Insurance Options Other Insurance Options, such as Pet & Legal Insurance Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support Flexible Spending Account 401(k) Employee Assistance Program Flexible PTO Paid Holidays Parental Leave Other In-Office Perks Equal Employment Opportunity TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law. Reasonable Accommodations TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact . Employment Eligibility All offers of employment are contingent upon verification of identity and authorization to work in United States, as required by law. Background Checks Where permitted by law, employment may be contingent upon the successful completion of a job-related background check. Data Privacy Notice By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.