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