Kubernetes Engineer

Kubernetes Engineer at Tensormesh

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 Dev Ops role, this position involves deep software development within the Kubernetes ecosystem. You'll extend Kubernetes capabilities by writing custom operators and controllers that manage complex AI inference workloads, ensuring high availability and seamless auto-scaling.

• 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.

• 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).
• Experience with service meshes (Istio, Linkerd) or ingress controllers (Nginx, Traefik) is a plus.
• Understanding of distributed consensus and state management.

• Competitive base salary
• Performance-based bonus
• Equity options
• Medical, dental, and vision insurance
• 401(k) retirement plan
• Paid time off

• Build infrastructure powering next-generation AI applications
• Work alongside a highly technical and experienced engineering team
• Make a direct impact in a fast-growing startup environment
• Take ownership of challenging technical problems at scale

Location:

Hybrid (Foster City, CA)

Tensormesh helps teams slash AI inference costs and latency by up to 10x with enterprise-grade caching for large language models.

Not available from the provided company information.

Not available from the provided company information.

• Work on high-impact infrastructure that targets two of the biggest pain points in production LLM systems: cost and latency.
• Build enterprise-grade systems focused on performance, reliability, and real-world deployment needs.

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