Senior Infrastructure Engineer

Department: Engineering

Location: San Francisco

• Provision and manage multi-region Kubernetes clusters across AWS and GPU cloud providers using infrastructure-as-code.
• Own the GitOps deployment lifecycle (Helm charts, Kustomize overlays, image automation, and continuous delivery.)
• Manage GPU node infrastructure: scheduling, model weight caching, image prefetching for fast cold starts, and GPU observability.
• Operate and improve our networking layer: ingress and gateway management, load balancing, media relay infrastructure, and cross-region connectivity.

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• Build and maintain our observability stack: metrics, logs, traces, and profiling across all services and GPU workloads.
• Maintain infrastructure security: IAM, secret management, certificate automation, and encryption at rest.
• Own CI/CD pipelines for monorepo builds spanning Go services, Python model containers, and Helm chart releases.
• Partner with ML engineers on model serving: container optimization, health checks and startup tuning, media pipeline performance, and multi-GPU configuration.
  
  

• You've operated Kubernetes in production at scale, not just deployed to it, but debugged node-level scheduling issues, tuned autoscalers, and managed cluster upgrades.
• Strong infrastructure-as-code experience (Terraform, Pulumi, or similar) across multiple environments and regions.
• You've worked with GPU workloads on Kubernetes: device plugins, node taints/tolerations, GPU-aware scheduling. You understand why bin-packing matters
  for expensive hardware.

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• Experience with GitOps tooling (FluxCD, ArgoCD, or similar) and Helm chart authoring.
• Comfortable with Redis or similar in-memory data stores (replication, persistence, pub/sub or streaming patterns)
• Familiarity with modern observability stacks (Prometheus, Grafana, OpenTelemetry, or equivalent) and knowing when to reach for metrics vs. logs vs. traces.
• Solid networking fundamentals: load balancers, TLS, DNS, NAT. Real-time or low-latency networking experience is a strong plus.
• You've worked in a startup where you owned infrastructure end-to-end, not just one slice of it.

• Experience with GPU cloud providers beyond AWS (Crusoe, CoreWeave, Lambda Labs, Nebius)
• Real-time media or streaming infrastructure
• Go or Python proficiency
• Familiarity with ML model serving (container image optimization, weight loading, GPU driver and runtime management)

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• FinOps and GPU cost optimization

• Pure CI/CD pipeline engineers who haven't operated Kubernetes clusters directly
• Candidates whose infrastructure experience is limited to managed PaaS (Heroku, Vercel, Railway)
• People who need a fully defined scope, this role requires figuring out what to build next, not just executing tickets
  
  

• Competitive San Francisco salary and meaningful equity
• We sponsor visas and support relocation to the US
• Generous health, dental, and vision coverage