Quick overview
Job description
About the Role
Join an AI/ML infrastructure team to lead end-to-end deployments of machine learning software in enterprise customer environments, from cloud VPCs to on-premises hardware. You will work directly with customer engineering teams to make complex deployments reliable, secure, and repeatable.
What You'll Do
Plan, configure, test, and roll out deployments across customer cloud and on-premises environments.
Partner with customer IT, security, and platform teams, including through live channels and on-site visits when needed.
Diagnose and resolve infrastructure issues involving Kubernetes configuration, cloud IAM, and networking.
Build monitoring, telemetry, and automation that turn one-off deployment work into reusable patterns.
Understand the hardware requirements of machine learning models and deploy them on customer-controlled infrastructure.
What We're Looking For
At least 3 years of experience in infrastructure engineering, DevOps, or SRE, with experience supporting production systems.
Deep production expertise in Kubernetes and Terraform, plus experience with AWS, GCP, or Azure, networking, and infrastructure as code.
Familiarity with Python, Helm, Docker, Linux, GPU infrastructure, and customer-managed deployment tooling is valuable.
Experience shipping software into customer-controlled environments, such as VPC, air-gapped, on-premises, or customer-managed Kubernetes deployments.
Strong communication skills and the ability to troubleshoot issues live with external engineering teams.
A strong record of technical ownership, such as high-growth startup experience, significant open-source contributions, rapid career growth, or founding-engineer scope.
Compensation & Benefits
Base salary: $200,000 to $300,000 annually. Visa sponsorship is available.
Location
On-site in San Francisco, California, United States.
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