Staff Software Engineer - Cloud Platform— Kubernetes, Hyperscalers & Distributed Systems

To be successful in this role you have: You will own the design and delivery of significant components and core subsystems of our Kubernetes platform—for example secrets and certificate management, workload identity, storage, or cluster networking—from design through production operation. You will take a loosely defined platform problem and turn it into a design, a plan, and shipped software, with limited scaffolding from others. You will contribute to the architecture of distributed workloads running on the platform, working with the teams who depend on it to get the runtime and isolation model right. You will spend most of your time hands-on in code—operators and controllers, infrastructure automation, and platform services—and in the reviews that keep their quality high. You will own the operability of what you build: SLOs, failure modes, upgrade and migration paths, and on-call for your own systems. You will partner with senior staff and principal engineers to keep your work aligned with the wider platform architecture, and mentor engineers earlier in their careers. You will help us run a platform that serves regulated markets, where compliance constraints including FedRAMP shape design choices.

To be successful in this role you have: Built and operated Kubernetes platform infrastructure in production before, at meaningful scale—not just consumed it. You can point to specific subsystems you designed, shipped, and then supported through real incidents and upgrade cycles. 8+ years building production software, including solid experience operating distributed systems. Strong programming skills in Go, with production Kubernetes controller or operator work behind you. Hands-on depth with at least one major hyperscaler (AWS, Azure, GCP)—its compute, networking, and IAM primitives, and where they leak into cluster design. A track record of owning complex components end to end, from ambiguous problem to production, whether or not you carried a lead title. Strong working knowledge of containers, CI/CD and GitOps-based delivery, and infrastructure-as-code. Experience leveraging or critically thinking about how to integrate AI into engineering and platform work—AI-powered tooling, automated operational workflows, agentic systems for fleet visibility and operations, or reasoning about AI’s impact on how infrastructure is built and run. It also helps if you have: Experience with managed Kubernetes (EKS/AKS/GKE) alongside self-managed clusters. Experience with container networking (CNI) and/or service mesh. Experience with mTLS, workload identity, and secrets management at fleet scale. Experience with observability—metrics, tracing, and SLOs. Experience with Terraform, Crossplane, or similar declarative infrastructure tooling. Experience designing or tuning distributed data or compute workloads on Kubernetes.

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