Principal / Distinguished Engineer (Multi-Cloud, Control Plane, Kubernetes)

Join our team as a Principal / Distinguished Engineer, where you will have org-wide technical authority and the ability to set technical direction across multiple engineering organizations. You will own the hardest, most ambiguous problems in the platform domain and partner with engineering fellows, principal engineers, and other senior technical leaders to drive consistent architectural decisions and the adoption of best practices. You will also mentor staff and principal engineers and shape the next generation of the organization’s technical leadership.

• Set the technical direction for the cloud-native platform across multiple engineering teams, defining the architecture and standards for Kubernetes and distributed systems.
• Own the hardest, most ambiguous technical problems in the platform domain, including multi-cloud topology, control-plane design, workload isolation, and reliability at scale.
• Partner with engineering fellows and senior technical leaders to drive consistent architectural decisions and the adoption of best practices across the platform ecosystem.
• A proven track record of providing technical leadership across multiple engineering teams, influencing architecture and direction without relying on positional authority
• 12+ years of experience designing, building, and operating large-scale distributed systems in production, with deep expertise running Kubernetes at scale (multi-cluster, multi-region, multi-tenant)
• Deep expertise in the core building blocks of a modern platform: the operator/controller pattern, infrastructure-as-code and control planes (e.g., Crossplane), GitOps-based delivery, container networking (CNI), and service mesh
• Strong programming skills in Go and fluency across the cloud-native ecosystem
• Experience leveraging or critically thinking about how to integrate AI into engineering and platform work — whether using AI-powered tooling, automating operational workflows, building agentic systems for fleet visibility and operations, or reasoning about AI’s impact on how infrastructure is built and run
• Hands-on, authoritative experience across one or more major hyperscalers (AWS, Azure, GCP), including their managed Kubernetes offerings (EKS, AKS, GKE) and the networking, IAM, and capacity tradeoffs that come with each
• Experience designing identity and trust fabrics for distributed systems — workload identity, mTLS, and standards such as SPIFFE/SPIRE
• Experience building and operating platforms for regulated or federal markets (FedRAMP, air-gapped or self-hosted distribution, OCI bundling)
• Experience with multi-tenant workload isolation and runtime security (e.g., Kata Containers, sandboxed runtimes)
• Experience with observability at scale — metrics, tracing, and SLO-driven operations across a large fleet
• A platform-as-product mindset: treating internal engineering teams as customers and the platform as a product with a roadmap, contracts, and a delivery pipeline