Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale

What you'll do

• Lead performance and scalability analysis across the Kubernetes-based accelerated runtime stack from orchestration down to the metal.
• Design and contribute upstream architectural changes to the Kubernetes control plane for hyperscale cluster operations.
• Improve container startup and cold-start latency to enable low-latency inference scaling across thousands of GPU nodes.
• Contribute to open-source projects to optimize Kubernetes for multi-node AI training and inference workloads.
• Advance the scalability and performance of confidential containers to meet production efficiency and latency requirements.
• Use large-scale simulation infrastructure to model AI-factory deployments and validate scalability across thousands of simulated GPUs.
• Design automated workload tests and integrate continuous performance testing into modern CI/CD workflows.
• Document technical findings and engage with upstream communities to influence AI workload performance and scalability directions.

What we're looking for

• Bachelor’s or Master’s degree in Engineering or equivalent experience, ideally in Electrical, Computer Engineering, or Computer Science.
• 5+ years of experience in computer architecture, networking, storage systems, and accelerator-based platforms.
• Expertise in Kubernetes and familiarity with the broader CNCF ecosystem.
• Deep experience with large-scale, parallel, distributed accelerator systems and performance optimization of AI workloads.
• Experience with performance modeling and benchmarking for large-scale systems.
• Proficiency in Golang and/or Python.
• Strong familiarity with the NVIDIA software stack across training and inference.
• Expertise with at least one major public cloud provider (e.g., AWS, Azure, GCP, or OCI).
• Strong operational experience with any one of the Kubernetes distributions (preferred).
• Prior experience scaling Kubernetes clusters to ultra-large node and object counts (preferred).
• Demonstrated history of working in the open-source community (preferred).
• Excellent communication and interpersonal abilities (preferred).
• PhD or equivalent experience in relevant areas (preferred).