Lambda

Senior Platform Engineer - Core Infrastructure

Full-time · San Francisco Office (Fremont St) (Remote)
✓ Verified live on the employer's own system · added 20 days ago
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Mid-level · 5+ yrs exp

Requirements

Experience: 5+ years

Skills & tools

DevopsCloud PlatformsEmbeddedManagementSecurityRoot Cause AnalysisTeam LeadershipOperations

Benefits — mentioned in this posting

Equity / stockHealth, dental & vision401(k) / retirementPaid time off
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Full job description

What You'll Do - Architect, deploy, and operate Kubernetes clusters across AWS and Lambda's bare-metal datacenters. - Build and maintain automation for cluster lifecycle management - provisioning, upgrades, and scaling. - Own the reliability, performance, and security of Kubernetes workloads in production. - Implement observability, logging, and alerting for clusters and critical workloads. - Partner with product teams to design scalable, cloud-native services and CI/CD pipelines. - Set the standards for resource management, networking, and RBAC across the platform. - Lead incident response, root-cause analysis, and post-mortems for platform issues. - Mentor engineers and raise the bar for platform engineering across the org.

You - 5+ years in Platform, Infrastructure, or SRE roles, including running Kubernetes in production at scale. - Deep knowledge of Kubernetes internals and day-2 operations (upgrades, scaling, troubleshooting). - Strong with Helm, Kustomize, or similar, and GitOps-based delivery. - Proficient with infrastructure-as-code (Terraform, Pulumi, or equivalent). - Solid grounding in networking, service meshes, and container runtimes. - Hands-on with observability stacks (Prometheus, Grafana, OpenTelemetry). - Strong coding skills in Go or Python for automation and tooling. - Practical security experience: network policies, secrets management, and image scanning.

Nice to Have - Experience with multi-cluster, multi-cloud, or hybrid environments. - Knowledge of GPU scheduling, HPC workloads, or ML/AI infrastructure. - Experience with workflow orchestration / durable execution frameworks (Temporal, Cadence, or Argo Workflows). - Exposure to cost optimization and capacity planning for large clusters. - Contributions to CNCF or Kubernetes open-source projects. - CKA/CKS certification.

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