Tensorwave

Staff Infrastructure Engineer – Kubernetes Platform

Las Vegas, Nevada, United StatesRemoteFull timeStaffPosted 12 days ago
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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 p...