About the Role

Join an AI infrastructure team deploying machine learning software into enterprise customer environments, from cloud VPCs to on-premises hardware. You will own deployments end to end and work directly with customer IT, security, and platform teams to make complex installations reliable and repeatable.

What You'll Do

Plan, configure, test, and roll out deployments across customer cloud and on-premises environments.

Partner with customer engineers and technical leaders, including through live calls and shared communication channels.

Diagnose and resolve infrastructure issues involving Kubernetes configuration, cloud IAM, and networking.

Build monitoring, telemetry, and automation that turn one-off deployment work into repeatable patterns.

Understand the hardware used for machine learning workloads and deploy those workloads on customer-controlled infrastructure.

What We're Looking For

At least 3 years of experience in infrastructure engineering, DevOps, or SRE.

Deep production experience with Kubernetes and Terraform, plus hands-on expertise with AWS, GCP, or Azure, networking, and infrastructure as code.

Experience with tools and technologies such as Python, Helm, Docker, Linux, GPU infrastructure, and customer-managed deployment environments.

Experience shipping software into customer-controlled environments, such as VPC, air-gapped, on-premises, or customer-managed cluster deployments.

Clear, structured communication and the ability to diagnose technical issues live with external engineering teams.

Experience in a startup or high-growth technology environment and a strong record of engineering ownership.

Compensation & Benefits

Base salary: $200,000 to $300,000 annually. Visa sponsorship is available.

Location

On-site in San Francisco, California, United States.