Principal Software Engineer

As a Principal Engineer, you will set the technical direction for Atlassian's Kubernetes, compute and AI/ML infrastructure platform. You will lead engineers across several teams on our hardest problems, and take large, cross-team initiatives from design to launch.

What you'll do

• Define the architecture and multi-year roadmap for a multi-cloud, cell-based Kubernetes fleet that scales to thousands of clusters.

• Design Kubernetes-native control planes (controllers, operators, CRDs and policy) that give engineers self-serve compute, networking and cloud resources.

• Raise platform reliability and security: reduce blast radius, build safe progressive rollouts, define SLOs, isolate workloads and support compliance (for example FedRAMP).

• Lead compute efficiency through autoscaling, bin packing, capacity planning and cost optimisation.

• Build the AI/ML infrastructure ecosystem on Kubernetes: GPU compute at scale, high-bandwidth, low-latency networking, and high-performance parallel filesystems for training and inference.

• Influence engineering and product leaders globally, set architectural standards, and mentor senior engineers.

• 10+ years building and operating large-scale cloud infrastructure or platform-as-a-service systems.

• Deep, hands-on Kubernetes expertise from running it in production at scale: internals, networking, multi-tenancy, and building operators and CRDs.

• Strong experience with AWS and/or GCP (EKS/GKE) and distributed systems design.

• Strong programming skills in one or more languages such as Go, Python, Java or similar.

• Platform engineering experience with Infrastructure as Code, GitOps/CD (for example Terraform, Crossplane, ArgoCD) and observability.

• A track record of leading technical direction across teams, with excellent communication and mentoring skills.

Nice to have

• Experience with Kubernetes fleet management, cell-based architectures, sandboxing (gVisor, Firecracker) or regulated environments.

• Experience providing AI/ML infrastructure: GPU clusters, high-bandwidth networking (for example RDMA, InfiniBand, AWS EFA, GPUDirect) or parallel filesystems (for example Lustre, Amazon FSx for Lustre, GCP Parallelstore).

• Contributions to CNCF open-source projects.