Senior MLOps Engineer: Kubernetes & ML Infrastructure Lead

Intuitive Surgical, Inc.; Sunnyvale (CA); On-site; USD 189,000 - 271,000; Full time

Job summary

Intuitive Surgical, Inc. is seeking an experienced infrastructure/DevOps engineer to design, build, and operate the ML lifecycle infrastructure.

You will ensure seamless integration of ML models into production systems and collaborate with ML engineers and software teams to drive scalable, reliable platforms. The role emphasizes autonomy, strong problem-solving, and hands-on work across Kubernetes, CI/CD, storage, and security.

Qualifications

• 3+ years in infrastructure, DevOps, or MLOps roles or equivalent experience.
• Production Kubernetes experience: networking, storage, RBAC, troubleshooting.
• Strong Python/Bash scripting; comfort with IaC tools (Ansible, Helm, Terraform).
• Hands-on with distributed storage systems (S3, MinIO, NetApp).
• Experience building/maintaining CI/CD pipelines (GitLab CI, ArgoCD).
• Solid Linux and networking fundamentals.

Responsibilities

• Bootstrap and maintain a production-grade Kubernetes cluster with CNI networking and storage integration.
• Deploy and configure ML orchestration tooling and storage to support reproducible ML workflows.
• Validate GPU node health and configuration across heterogeneous hardware and drivers.
• Design and execute migration playbooks; port workflows and roll out updates.
• Write and maintain runbooks, architecture docs, and disaster recovery procedures.
• Participate in on-call rotation and incident response for platform issues.
• Collaborate with IT/Security on identity integration and access control.
• Continuously evaluate infrastructure best practices for reliability and cost.

Skills

Kubernetes

Python

Linux

CI/CD

Ansible

Terraform

Helm

GitLab CI

ArgoCD

Scripting

Education

Bachelor's or Master's

Tools

Kubernetes

Job description

Intuitive Surgical, Inc. is seeking an experienced infrastructure/DevOps engineer to design, build, and operate the ML lifecycle infrastructure.

You will ensure seamless integration of ML models into production systems and collaborate with ML engineers and software teams to drive scalable, reliable platforms. The role emphasizes autonomy, strong problem-solving, and hands-on work across Kubernetes, CI/CD, storage, and security.