Senior Platform Engineer (Core Infrastructure)

Lambda; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Health, dental vision

401k match

5 sick days and 12 paid holidays

Flexible PTO

Parental, medical, caregiver leave

Job summary

Lambda is seeking a senior Platform/SRE engineer to architect and run Kubernetes at scale across AWS and bare-metal data centers. You will own cluster reliability, performance, security, and automation, partnering with product teams to deliver scalable cloud-native services and robust CI/CD pipelines.

Ideal candidates have 5+ years in platform or SRE roles, deep Kubernetes knowledge, Go or Python scripting ability, and strong networking/security fundamentals.

Qualifications

• 5+ years in Platform, Infrastructure, or SRE roles, incl. running Kubernetes in production at scale.
• Deep knowledge of Kubernetes internals and day-2 operations (upgrades, scaling, troubleshooting).
• Strong coding skills in Go or Python for automation and tooling.
• Experience with multi-cluster, multi-cloud, or hybrid environments.
• Solid grounding in networking, service meshes, and container runtimes.
• Practical security experience: network policies, secrets management, and image scanning.

Responsibilities

• Architect, deploy, and operate Kubernetes clusters across AWS and Lambda’s datacenters.
• Build and maintain automation for cluster lifecycle management — provisioning, upgrades, and scaling.
• Own 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 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.

Skills

Kubernetes

Observability

Terraform

Pulumi

Go

Python

GitOps

Multi-cluster

Cost optimization

Security best practices

Networking

RBAC

Tools

Helm

Kustomize

Argo Workflows

Temporal

Cadence

Job description

• Engineering at Lambda is responsible for building and scaling our cloud offering. Our scope includes the Lambda website, cloud APIs and systems as well as internal tooling for system deployment, management and maintenance
• 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

Benefits

• Health, dental vision
• 401k match
• 5 sick days and 12 paid holidays
• Flexible PTO
• Paid parental, medical, and caregiver leave

Hands-on with observability stacks (Prometheus, Grafana, OpenTelemetry)Solid grounding in networking, service meshes, and container runtimesProficient with infrastructure-as-code (Terraform, Pulumi, or equivalent)Deep knowledge of Kubernetes internals and day-2 operations (upgrades, scaling, troubleshooting)5+ years in Platform, Infrastructure, or SRE roles, including running Kubernetes in production at scaleStrong with Helm, Kustomize, or similar, and GitOps-based deliveryPractical security experience: network policies, secrets management, and image scanningStrong coding skills in Go or Python for automation and toolingKnowledge of GPU scheduling, HPC workloads, or ML/AI infrastructureExperience with multi-cluster, multi-cloud, or hybrid environmentsExperience with workflow orchestration / durable execution frameworks (Temporal, Cadence, or Argo Workflows)Exposure to cost optimization and capacity planning for large clustersContributions to CNCF or Kubernetes open-source projectsCKA/CKS certification