Lead Platform Engineer, Kubernetes & AI Infrastructure

Edison Scientific, Inc.; San Francisco (CA); On-site; USD 200,000 - 350,000; Full time

Benefits offered by this job

Full healthcare coverage

Parental leave (12 weeks)

Mental health support

401(k) matching

Wellness benefit

Lunch provided in office

Team off-sites

Job summary

Edison Scientific, Inc. is building an AI-driven platform and seeks a Principal Member of Technical Staff to own the core infrastructure that runs autonomous scientific workloads.

You will architect and operate Kubernetes clusters, design CRDs and operators, and ensure resilient, scalable compute for thousands of long‑running tasks. You’ll collaborate with backend, ML, and research teams, implement infrastructure-as-code, and drive best practices for observability, security, and cost efficiency.

Qualifications

• Over 10 years of infra or platform engineering in production.
• Deep Kubernetes internals knowledge (API server, etcd, scheduler).
• Experience with CRDs and Kubernetes operators.
• Hands-on with IaC tools (Terraform, Pulumi, Crossplane).
• Familiar with cloud providers AWS/GCP/Azure networking, storage, IAM.

Responsibilities

• Architect, implement, and operate Kubernetes clusters at scale.
• Design CRDs and operators for domain workloads.
• Drive cluster scaling, autoscaling, and resource quotas.
• Manage IaC for reproducible environments.
• Design scheduling and affinity for heterogeneous workloads.
• Establish observability, monitoring, and incident response.

Skills

Kubernetes expertise

Cloud infra design

CRDs & Operators

Terraform/Pulumi

GitOps

Tools

Kubebuilder

Operator SDK

controller-runtime

Terraform

Pulumi

Crossplane

Job description

Edison Scientific, Inc. is building an AI-driven platform and seeks a Principal Member of Technical Staff to own the core infrastructure that runs autonomous scientific workloads.

You will architect and operate Kubernetes clusters, design CRDs and operators, and ensure resilient, scalable compute for thousands of long‑running tasks. You’ll collaborate with backend, ML, and research teams, implement infrastructure-as-code, and drive best practices for observability, security, and cost efficiency.