Staff Infrastructure Engineer – Kubernetes Platform

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.

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.

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

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

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, CNI behavior (Cilium preferred)

Collaborate with network engineering on high-performance networking integration

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

Work closely with DevOps engineers (automation and CI/CD) and Infrastructure teams (compute, storage, networking)

Align Kubernetes platform design with underlying infrastructure capabilities

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

Multiple clusters

Multiple regions or data centers

Strong understanding of Kubernetes internals: API server Scheduler Controller manager etcd

API server

Scheduler

Controller manager

etcd

Experience designing or evolving: Control plane architectures Multi-tenant cluster models

Control plane architectures

Multi-tenant cluster models

Strong Linux systems expertise

Deep troubleshooting ability across: Kubernetes Container runtime Networking stack

Kubernetes

Container runtime

Networking stack

Experience with CNI plugins (Cilium preferred)

Strong understanding of: Networking and traffic patterns Resource isolation and scheduling

Networking and traffic patterns

Resource isolation and scheduling

Experience with virtual cluster technologies (vcluster, Kamaji, or similar)

Experience supporting GPU workloads in Kubernetes

Familiarity with: NUMA-aware scheduling Topology-aware workloads

NUMA-aware scheduling

Topology-aware workloads

Awareness of RDMA and high-throughput networking environments

Experience with observability platforms (Prometheus, Grafana, etc.)

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.

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