Senior Kubernetes & ML Infra Engineer (Multi-Cloud)

TypeSafe AI; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Base salary 150k-250k

Equity

Health insurance

Lunch provided

Visa sponsorships

401K

Job summary

TypeSafe AI, an AI lab in San Francisco, is seeking an Infrastructure Engineer to build and operate the infrastructure behind its products at global scale. You’ll own systems serving millions of users, provisioning Kubernetes clusters across clouds and optimizing AI inference networking.

You’ll work across the full stack—from cloud primitives to observability—with a team known for speed and impact, shaping the foundation for reliable, production-grade ML infrastructure.

Qualifications

• Deep hands-on experience running Kubernetes in production at scale.
• Strong background in AWS cloud services.
• Hands-on experience with infrastructure as code (Pulumi, Terraform, or similar).
• Solid understanding of Linux networking.
• Track record with high-traffic production ML systems.

Responsibilities

• Design, deploy, and operate Kubernetes clusters across multiple regions and clouds.
• Build and maintain infrastructure for the platform that powers LLM inference workloads globally.
• Own networking, including VPCs, peering, load balancing, DNS, service mesh, and CNI.
• Manage GPU infrastructure and autoscaling for ML workloads.
• Write and maintain infrastructure as code (Pulumi / Python).
• Operate and improve observability: monitoring, alerting, tracing, logging.

Skills

Kubernetes

AWS

IaC

Linux networking

ML systems

Python

Tools

Pulumi

Terraform

Job description

TypeSafe AI, an AI lab in San Francisco, is seeking an Infrastructure Engineer to build and operate the infrastructure behind its products at global scale. You’ll own systems serving millions of users, provisioning Kubernetes clusters across clouds and optimizing AI inference networking.

You’ll work across the full stack—from cloud primitives to observability—with a team known for speed and impact, shaping the foundation for reliable, production-grade ML infrastructure.