Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale

What you'll do

• Lead end-to-end 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 to enable reliable operation at hyperscale cluster sizes.
• Improve container startup and cold-start latency to ensure low-latency inference scaling across thousands of GPU nodes.
• Assess and improve open-source projects to optimize Kubernetes as a platform for large-scale AI training and inference.
• Advance the scalability and performance of confidential containers to meet strict efficiency requirements for encrypted workloads.
• Use simulation infrastructure to model full AI-factory deployments and validate scalability before hardware deployment.
• Develop automated, at-scale workload tests and integrate continuous performance testing into modern CI/CD workflows.
• Document findings and present technical results at internal meetings and major industry events like KubeCon and GTC.

What we're looking for

• Bachelor's or Master's degree in Engineering (ideally Electrical, Computer Engineering, or Computer Science) or equivalent experience.
• 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 such as AWS, Azure, GCP, or OCI.