What You’ll Do

• Design and implement AI inference and model training cloud products optimized for Kubernetes – from autoscaling inference servers to distributed training jobs across GPU fleets.
• Write clean, efficient, and maintainable Go code to power Kubernetes controllers, operators, and custom resources supporting AI workloads.
• Build APIs, CLIs, and developer tools that simplify the deployment, lifecycle management, and monitoring of AI applications.
• Develop features that optimize serverless container workflows for AI, ensuring fast cold starts, resource-efficient scaling, and workload isolation.
• Contribute to system performance, reliability, and security, with a focus on AI-specific challenges such as GPU scheduling, job orchestration, and data throughput.
• Stay on top of Kubernetes ecosystem advancements (e.g., K8s-native ML tooling, scheduling improvements, SIGs) and influence our product roadmap accordingly.