Responsibilities
- Design, build, and operate components of our Kubernetes platform and its core subsystems, owning a well-scoped area end to end
- Write and review a lot of Go—controllers, operators, platform services, and the automation around them
- Take a defined problem with open design questions and drive it to a working, production-ready answer, checking in on direction rather than needing it handed to you
- Help operate what the team builds: on-call, incident investigation, and the follow-up work that stops the same failure twice
- Contribute to how workloads run on the platform, working with the teams that depend on it
- Work on a platform that serves regulated markets, where compliance constraints including FedRAMP shape design choices
Requirements
- Experience leveraging or critically thinking about how to integrate AI into engineering work — whether using AI-powered coding and operational tooling, automating workflows, or reasoning about how AI changes the way software and infrastructure are built
- Already built and shipped Kubernetes infrastructure or platform components in production. A smaller scope than a whole platform is fine, but it needs to be something you designed and owned, not something you configured or consumed
- 5+ years building production software, with real experience running workloads on Kubernetes and debugging them when they misbehave
- Solid programming skills in Go, or strong systems-language skills and a clear willingness to work primarily in Go
- Working experience with at least one major hyperscaler (AWS, Azure, GCP), including its core compute, networking, and IAM primitives
- Comfort with containers, CI/CD and GitOps-based delivery, and infrastructure-as-code
- Curiosity about how AI changes platform engineering—using AI-powered tooling, automating operational work, or thinking about what it means for how infrastructure is built and run
Nice to Have
- Experience building Kubernetes controllers or operators with the operator pattern
- Exposure to container networking (CNI), service mesh, or workload identity and mTLS
- Experience with secrets management, certificate lifecycle, or similar core platform subsystems
- Experience with observability tooling—metrics, tracing, and SLOs
- Experience with managed Kubernetes (EKS/AKS/GKE), Terraform, or Crossplane
