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Clera

Platform Engineer (Kubernetes)

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join a seed-stage AI infrastructure startup as a Platform Engineer to build GitOps-native Kubernetes tooling that simplifies multi-cluster management for GPU workloads. You'll work with the founding team to design core platform features in Go, from bare metal provisioning to production-grade AI cluster orchestration.

What you'll do

  • Build custom Kubernetes operators and controllers in Go for core platform capabilities
  • Design and implement GitOps workflows using ArgoCD for seamless continuous deployment
  • Develop infrastructure-as-code with Terraform and Helm for cluster provisioning and management
  • Architect federated Kubernetes systems for multi-cluster coordination and management
  • Create observability solutions with Prometheus and Grafana for cluster health visibility
  • Optimize container networking and security using Cilium and related technologies

What they're looking for

  • Go programming
  • Kubernetes (operators, controllers, CRDs)
  • ArgoCD and GitOps workflows
  • Terraform and Helm
  • Distributed storage (Ceph, WEKA)
  • Cilium and container networking
  • Prometheus and Grafana
  • Cloud platforms (AWS, GCP, Azure)

Benefits

  • Base salary $150,000–$200,000 USD annually
  • Visa sponsorship available
  • Early-stage equity opportunity
  • Work with founding team on architectural decisions
  • On-site collaborative environment in San Francisco
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through your experience writing Kubernetes operators or controllers in Go—what was the most complex use case you tackled?
  • How have you managed Kubernetes clusters at meaningful production scale, and what scaling challenges did you encounter?
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