Kubernetes Engineer

Tensormesh•Foster City, CA

17h

About The Position

Tensormesh is building the next generation of AI inference infrastructure. Our mission is to make large language models faster, cheaper, and easier to deploy across any environment — cloud, on-prem, or hybrid. We help enterprises and AI teams optimize GPU utilization and scale inference workloads with up to 10× better performance. We are seeking a Kubernetes Engineer to build the core orchestration logic of the Tensormesh platform. Unlike a standard DevOps role, this position involves deep software development within the Kubernetes ecosystem. You will extend Kubernetes capabilities by writing custom operators and controllers that manage complex AI inference workloads, ensuring high availability and seamless auto-scaling.

Requirements

2-10 years of software engineering experience, with a focus on distributed systems or container orchestration.
Strong proficiency in Go (Golang); experience with the Kubernetes client-go library and Kubebuilder/Operator SDK is highly preferred.
Deep understanding of Kubernetes internals (API machinery, Controller runtime, Networking, CNI).
Understanding of distributed consensus and state management.

Nice To Haves

Experience with service meshes (Istio, Linkerd) or ingress controllers (Nginx, Traefik) is a plus.

Responsibilities

Develop Custom Operators: Design and implement Kubernetes Custom Resource Definitions (CRDs) and Controllers (using Golang/Kubebuilder) to manage the lifecycle of Tensormesh products.
Traffic Management: Architect and build high-performance ingress and load-balancing systems capable of handling high-throughput inference requests.
Resilience & Scaling: Develop logic for intelligent auto-scaling (HPA/VPA) based on GPU metrics and ensure High Availability (HA) for critical components.
K8s Optimization: Tune the scheduler and resource management configurations to maximize GPU utilization for inference tasks.